Distinguishing methylation levels in complex biological samples

ABSTRACT

Provided herein is a method for distinguishing an aberrant methylation level for DNA from a first cell type, including steps of (a) providing a test data set that includes (i) methylation states for a plurality of sites from test genomic DNA from at least one test organism, and (ii) coverage at each of the sites for detection of the methylation states; (b) providing methylation states for the plurality of sites in reference genomic DNA from one or more reference individual organisms, (c) determining, for each of the sites, the methylation difference between the test genomic DNA and the reference genomic DNA, thereby providing a normalized methylation difference for each site; and (d) weighting the normalized methylation difference for each site by the coverage at each of the sites, thereby determining an aggregate coverage-weighted normalized methylation difference score. Also provided herein are sensitive methods for using genomic DNA methylation levels to distinguish cancer cells from normal cells and to classify different cancer types according to their tissues of origin.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 62/401,591, filed Sep. 29, 2016, and U.S. Provisional ApplicationSer. No. 62/268,961, filed Dec. 17, 2015, each of which is incorporatedby reference herein.

BACKGROUND

The present disclosure relates to determination of methylation patternsin genomic DNA. Specific embodiments relate to prediction, diagnosis,prognosis and monitoring of various conditions based on genomicmethylation patterns.

Changes in cellular genetic information, such as mutations in genesequences which can affect gene expression and/or protein sequence, areassociated with many diseases and conditions. However, changes can alsooccur to genes that affect gene expression; changes caused by mechanismsother than genetic mutations. Epigenetics is the study of changes ingene expression caused by mechanisms other than changes in theunderlying DNA sequence, the methylation of DNA being one of thosemechanisms. Methylation of DNA, for example, the addition of a methylgroup to the 5 position of a cytosine pyrimidine ring or the positionalsixth nitrogen of an adenine purine ring, is widespread and plays acritical role in the regulation of gene expression in development anddifferentiation of diseases such as multiple sclerosis, diabetes,schizophrenia, aging, and cancers. In adult somatic cells, DNAmethylation typically occurs in regions where a cytosine nucleotide (C)is found next to a guanine nucleotide (G) where the C and G are linkedby a phosphate group (p), the linear construct being referred to as a“CpG” site. Methylation in particular gene regions, for example, in genepromoter regions, can augment or inhibit the expression of these genes.

DNA methylation is widespread and plays a critical role in theregulation of gene expression in development, differentiation anddisease. Methylation in particular regions of genes, for example theirpromoter regions, can inhibit the expression of these genes (Baylin andHerman (2000) DNA hypermethylation in tumorigenesis: epigenetics joinsgenetics. Trends Genet, 16, 168-174.; Jones and Laird (1999) Cancerepigenetics comes of age. Nat Genet, 21, 163-167.). Gene silencingeffects of methylated regions has been shown to be accomplished throughthe interaction of methylcytosine binding proteins with other structuralcompounds of the chromatin (Razin (1998) CpG methylation, chromatinstructure and gene silencing—a three-way connection. Embo J, 17,4905-4908.; Yan et al. (2001) Role of DNA methylation and histoneacetylation in steroid receptor expression in breast cancer. J MammaryGland Biol Neoplasia, 6, 183-192.), which, in turn, makes the DNAinaccessible to transcription factors through histone deacetylation andchromatin structure changes (Bestor (1998) Gene silencing. Methylationmeets acetylation. Nature, 393, 311-312.). Genomic imprinting in whichimprinted genes are preferentially expressed from either the maternal orpaternal allele also involves DNA methylation. Deregulation ofimprinting has been implicated in several developmental disorders (Kumar(2000) Rett and ICF syndromes: methylation moves into medicine. JBiosci, 25, 213-214.; Sasaki et al. (1993) DNA methylation and genomicimprinting in mammals. Exs, 64, 469-486.; Zhong et al. (1996) A surveyof FRAXE allele sizes in three populations. Am J Med Genet, 64,415-419.). The references cited above are incorporated herein byreference.

In vertebrates, the DNA methylation pattern is established early inembryonic development and in general the distribution of5-methylcytosine (5 mC) along the chromosome is maintained during thelife span of the organism (Razin and Cedar (1993) DNA methylation andembryogenesis. Exs, 64, 343-357.; Reik et al. (2001) Epigeneticreprogramming in mammalian development. Science, 293, 1089-1093, each ofwhich is incorporated herein by reference). Stable transcriptionalsilencing is important for normal development, and is associated withseveral epigenetic modifications. If methylation patterns are notproperly established or maintained, various disorders like mentalretardation, immune deficiency and sporadic or inherited cancers mayfollow.

Changes in DNA methylation have been recognized as one of the mostcommon molecular alterations in human neoplasia. Hypermethylation of CpGsites located in promoter regions of tumor suppressor genes is afrequent mechanism for gene inactivation in cancers. Hypomethylation ofgenomic DNA are observed in tumor cells. Further, a correlation betweenhypomethylation and increased gene expression has been reported for manyoncogenes. Monitoring global changes in methylation pattern has beenapplied to molecular classification of cancers, for example, genehypermethylation has been associated with clinical risk groups inneuroblastoma and hormone receptor status correlation with response totamoxifen in breast cancer.

In addition to playing an important role in cancer detection, a properunderstanding of genetic methylation patterns has been used to detectother conditions. The initiation and the maintenance of the inactiveX-chromosome in female eutherians were found to depend on methylation(Goto and Monk (1998) Regulation of X-chromosome inactivation indevelopment in mice and humans. Microbiol Mol Biol Rev, 62, 362-378,which is incorporated herein by reference). Rett syndrome (RTT) is anX-linked dominant disease caused by mutation of MeCP2 gene, which isfurther complicated by X-chromosome inactivation (XCI) pattern. Acurrent model predicts that MeCP2 represses transcription by bindingmethylated CpG residues and mediating chromatin remodeling (Dragich etal. (2000) Rett syndrome: a surprising result of mutation in MECP2. HumMol Genet, 9, 2365-2375, which is incorporated herein by reference).

Several technical challenges hinder development of methylation detectiontechniques into a robust and cost efficient screening tool. For example,the accuracy and affordability of currently available techniques can becompromised by impurities in samples that are to be tested. As a result,cumbersome and expensive purification techniques are often employed topurify a genomic sample from background nucleic acids. For example,tumor biopsy techniques are employed to physically separate tumortissues from healthy tissues. Depending upon the depth of the tissue inthe body of an individual, biopsy can require unpleasant and riskyharvesting procedures such as needle biopsy, endoscopy, bronchoscopy,colonoscopy or surgery. The presence of circulating tumor DNA in bloodprovides an attractive alternative to such biopsy techniques. However,circulating tumor DNA is typically present in low quantities and in abackground of a relatively large quantity of non-tumor DNA.

Thus there is a need for methods to distinguish methylation patterns incomplex genomic samples from particular tissues of interest (e.g. tumorDNA), often in a background of other genomic material from other tissues(e.g. circulating DNA). The methods and apparatus set forth hereinsatisfy this need and provide other advantages as well.

BRIEF SUMMARY

The present disclosure provides a method for distinguishing an aberrantmethylation level for DNA from a first cell type. The method can includesteps of (a) providing a test data set that includes (i) methylationstates for a plurality of sites from test genomic DNA from at least onetest organism, and (ii) coverage at each of the sites for detection ofthe methylation states; (b) providing methylation states for theplurality of sites in reference genomic DNA from one or more referenceindividual organisms, (c) determining, for each of the sites, themethylation difference between the test genomic DNA and the referencegenomic DNA, thereby providing a normalized methylation difference foreach site; and (d) weighting the normalized methylation difference foreach site by the coverage at each of the sites, thereby determining anaggregate coverage-weighted normalized methylation difference score.

Also provided is a method for distinguishing an aberrant methylationlevel for DNA from a sample containing DNA from a plurality of differentcell types, including steps of (a) providing a sample containing amixture of genomic DNA from a plurality of different cell types from atleast one test organism, thereby providing test genomic DNA; (b)detecting methylation states for a plurality of sites in the testgenomic DNA; (c) determining the coverage at each of the sites for thedetecting of the methylation states; (d) providing methylation statesfor the plurality of sites in reference genomic DNA from at least onereference individual, the at least one test organism and referenceindividual optionally being the same species; (e) determining, for eachof the sites, the methylation difference between the test genomic DNAand the reference genomic DNA, thereby providing a normalizedmethylation difference for each site; and (f) weighting the normalizedmethylation difference for each site by the coverage at each of thesites, thereby determining an aggregate coverage-weighted normalizedmethylation difference score.

In particular embodiments, this disclosure provides a method fordetecting a condition such as cancer. The method can include steps of(a) providing a mixture of genomic DNA from blood of an individualsuspected of having the condition (e.g. cancer), wherein the mixturecomprises genomic DNA from a plurality of different cell types from theindividual, thereby providing test genomic DNA; (b) detectingmethylation states for a plurality of sites in the test genomic DNA; (c)determining the coverage at each of the sites for the detecting of themethylation states; (d) providing methylation states for the pluralityof sites in reference genomic DNA from at least one referenceindividual, the reference individual being known to have the condition(e.g. cancer) or known to not have the condition (e.g. cancer); (e)determining, for each of the sites, the methylation difference betweenthe test genomic DNA and the reference genomic DNA, thereby providing anormalized methylation difference for each site; (f) weighting thenormalized methylation difference for each site by the coverage at eachof the sites, thereby determining an aggregate coverage-weightednormalized methylation difference score; and (g) determining that theindividual does or does not have the condition (e.g. cancer) based onthe aggregate coverage-weighted normalized methylation difference score.

The present disclosure also provides an alternative sensitive method fordistinguishing an aberrant methylation level for DNA from a first celltype. The method can include a first stage of establishing a methylationbaseline, including the steps of (a) providing methylation states for aplurality of sites in baseline genomic DNA from two or more normalindividual organisms; and (b) determining, for each of the sites, themean methylation level and standard deviation of methylation levels forthe baseline genomic DNA; a second stage of determining aggregatemethylation scores for a plurality of training samples, including thesteps of (c) providing a training set of normal genomic DNA samples fromtwo or more normal individual organisms that includes (i) methylationstates for a plurality of sites in the training set of normal genomicDNA samples, and optionally (ii) coverage at each of the sites fordetection of the methylation states; (d) determining, for each of thesites, the methylation difference between each normal genomic DNA sampleof the training set and the baseline genomic DNA, thereby providing anormalized methylation difference for each normal genomic DNA sample ofthe training set at each site; (e) converting the normalized methylationdifference for each normal genomic DNA sample of the training set ateach site into the probability of observing such a normalizedmethylation difference or greater, and optionally weighting theprobability of such an event; (f) determining an aggregate methylationscore for each normal genomic DNA sample of the training set to obtaintraining set methylation scores; and (g) calculating the meanmethylation score and standard deviation of the training set methylationscores; a third stage, which can be carried out before, after, orconcurrently with the second stage, of determining an aggregatemethylation score for a given test sample, including the steps of (h)providing a test data set that includes (i) methylation states for theplurality of sites from test genomic DNA from at least one testorganism, and optionally (ii) coverage at each of the sites fordetection of the methylation states; (i) determining, for each of thesites, the methylation difference between the test genomic DNA and thebaseline genomic DNA, thereby providing a normalized methylationdifference for the test genomic DNA; (j) converting the normalizedmethylation difference for the test genomic DNA at each of the sitesinto the probability of observing such a normalized methylationdifference or greater, and optionally weighting the probability of suchan event; and (k) determining an aggregate methylation score for thetest genomic DNA; and a fourth stage of (1) comparing the methylationscore of the test genomic DNA to the mean methylation score and standarddeviation of methylation scores in the training set of normal genomicDNA to determine the number of standard deviations the methylation scoreof the test genomic DNA is from the distribution of methylation scoresin the training set of normal genomic DNA.

Also provided is an alternative sensitive method for distinguishing anaberrant methylation level for DNA from a sample containing DNA from aplurality of different cell types. The method can include a first stageof establishing a methylation baseline, including the steps of (a)providing methylation states for a plurality of sites in baselinegenomic DNA from two or more normal individual organisms; and (b)determining, for each of the sites, the mean methylation level andstandard deviation of methylation levels for the baseline genomic DNA; asecond stage of determining aggregate methylation scores for a pluralityof training samples, including the steps of (c) providing a training setof normal genomic DNA samples from two or more normal individualorganisms that includes (i) methylation states for a plurality of sitesin the training set of normal genomic DNA samples, and optionally (ii)coverage at each of the sites for detection of the methylation states;(d) determining, for each of the sites, the methylation differencebetween each normal genomic DNA sample of the training set and thebaseline genomic DNA, thereby providing a normalized methylationdifference for each normal genomic DNA sample of the training set ateach site; (e) converting the normalized methylation difference for eachnormal genomic DNA sample of the training set at each site into theprobability of observing such a normalized methylation difference orgreater, and optionally weighting the probability; (f) determining anaggregate methylation score for each normal genomic DNA sample of thetraining set to obtain training set methylation scores; and (g)calculating the mean methylation score and standard deviation of thetraining set methylation scores; a third stage, which can be carried outbefore, after, or concurrently with the second stage, of determining anaggregate methylation score for a given test sample, including the stepsof (h) providing a mixture of genomic DNA from a test organism suspectedof having a condition associated with an aberrant DNA methylation level,wherein the mixture includes genomic DNA from a plurality of differentcell types from the test organism, thereby providing test genomic DNA;(i) detecting methylation states for the plurality of sites in the testgenomic DNA, and optionally determining the coverage at each of thesites for the detecting of the methylation states; (j) determining, foreach of the sites, the methylation difference between the test genomicDNA and the baseline genomic DNA, thereby providing a normalizedmethylation difference for the test genomic DNA; (k) converting thenormalized methylation difference for the test genomic DNA at each ofthe sites into the probability of observing such a normalizedmethylation difference or greater, and optionally weighting theprobability of such an event; and (l) determining an aggregatemethylation score for the test genomic DNA; and a fourth stage of (m)comparing the methylation score of the test genomic DNA to the meanmethylation score and standard deviation of methylation scores in thetraining set of normal genomic DNA to determine the number of standarddeviations the methylation score of the test genomic DNA is from thedistribution of methylation scores in the training set of normal genomicDNA.

In particular embodiments, this disclosure provides a method fordetecting a condition such as cancer. The method can include a firststage of establishing a methylation baseline, including the steps of (a)providing methylation states for a plurality of sites in baselinegenomic DNA from at least one normal individual organism; and (b)determining, for each of the sites, the mean methylation level andstandard deviation of methylation levels for the baseline genomic DNA; asecond stage of determining aggregate methylation scores for a pluralityof training samples, including the steps of (c) providing a training setof normal genomic DNA samples from two or more normal individualorganisms that includes (i) methylation states for a plurality of sitesin the training set of normal genomic DNA samples, and optionally (ii)coverage at each of the sites for detection of the methylation states;(d) determining, for each of the sites, the methylation differencebetween each normal genomic DNA sample of the training set and thebaseline genomic DNA, thereby providing a normalized methylationdifference for each normal genomic DNA sample of the training set ateach site; (e) converting the normalized methylation difference for eachnormal genomic DNA sample of the training set at each site into theprobability of observing such a normalized methylation difference orgreater, and optionally weighting the probability of such an event; (f)determining a methylation score for each normal genomic DNA sample ofthe training set to obtain training set methylation scores; and (g)calculating the mean methylation score and standard deviation of thetraining set methylation scores; a third stage, which can be carried outbefore, after, or concurrently with the second stage, of determining anaggregate methylation score for a given test sample, including the stepsof (h) providing a mixture of genomic DNA from a test organism suspectedof having the condition, wherein the mixture comprises genomic DNA froma plurality of different cell types from the test organism, therebyproviding test genomic DNA; (i) detecting methylation states for theplurality of sites in the test genomic DNA, and optionally determiningthe coverage at each of the sites for the detecting of the methylationstates; (j) determining, for each of the sites, the methylationdifference between the test genomic DNA and the baseline genomic DNA,thereby providing a normalized methylation difference for the testgenomic DNA; (k) converting the normalized methylation difference forthe test genomic DNA at each of the sites into the probability ofobserving such a normalized methylation difference or greater, andoptionally weighting the probability of such an event; and (l)determining a methylation score for the test genomic DNA; and a fourthstage of (m) comparing the methylation score of the test genomic DNA tothe mean methylation score and standard deviation of methylation scoresin the training set of normal genomic DNA to determine the number ofstandard deviations the methylation score of the test genomic DNA isfrom the distribution of methylation scores in the training set ofnormal genomic DNA.

The present disclosure provides a method for using methylation levels toidentify or classify a specific type of cancer in a test organism. Themethod can include a first stage of identifying specific cancers thatcan be used as a cancer type, including (a) providing a data set thatincludes methylation states for a plurality of sites from genomic DNAfrom clinical samples known to include a specific cancer; a second stageof selecting hypermethylated sites that includes (b) identifyinghypermethylated sites characteristic of a cancer type, including (i)determining a mean methylation level for each site in the genomic DNA ofthe clinical samples known to include the specific cancer, (ii)determining which sites meet a first threshold, a second threshold, or acombination thereof, where determining the first threshold includes (1)determining the absolute value of the mean methylation level of eachsite; (2) ranking the mean methylation levels for each site from lowestto highest, and (3) selecting those sites having a mean methylationlevel at a percentile rank that is greater than or equivalent to a firstpreselected value, and where determining the second threshold includes(1) determining the absolute value of the mean methylation level of eachsite; and (2) selecting those sites having a mean methylation level thatis greater than a second preselected value, and (iii) compiling a listof hypermethylated sites that are characteristic for the cancer type;and (c) repeating (a) and (b) for each specific cancer, to result in aplurality of lists of hypermethylated sites that are characteristic foradditional cancer types; a third stage that includes analyzing a testgenomic DNA sample from a test organism by (d) providing a test data setthat includes a methylation level for each hypermethylated site from atest genomic DNA from an individual test organism, wherein thehypermethylated sites are from one of the lists of hypermethylated sitesthat is characteristic for a cancer type identified in steps (b) and(c); (e) averaging the methylation level of each of the hypermethylatedsites to result in a single average methylation level for the testgenomic DNA for the cancer type identified in steps (b) and (c); (f)repeating step (e) for each cancer type, to result in an averagemethylation level for each cancer type; and (g) ranking the averagemethylation levels for each cancer type from lowest to highest, whereinthe cancer type corresponding to the highest average methylation levelis the cancer present in the individual test organism.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows criteria for designing probes to regions of a genome havingmethylation sites.

FIG. 2 shows a workflow for targeted circulating tumor DNA (ctDNA)methylation sequencing.

FIG. 3 shows aggregate coverage-weighted normalized methylationdifferences (z-scores) determined as described herein for various cancersamples at various titration levels.

FIG. 4 shows coverage-weighted methylation scores determined asdescribed herein for colorectal cancer samples at various titrationlevels.

FIG. 5 shows methylation scores for 66 samples from advanced cancerpatients and 25 samples from normal individuals, demonstrating theability of the methylation score algorithm to distinguish advancedcancer samples from normal samples.

FIG. 6 shows a tabulated summary of the methylation scores shown in FIG.5.

FIG. 7 shows correlation of methylation profiles between plasma andtissue DNA samples from cancer patients.

FIG. 8 shows cancer type classification results on tumor tissue samples,demonstrating the ability of the cancer type classification algorithm toidentify most tumors based on DNA methylation data with a high degree ofaccuracy.

FIG. 9 depicts cancer type classification results on plasma DNA samplesfrom cancer patients, showing high clinical sensitivities for colorectaland breast cancers.

DETAILED DESCRIPTION

DNA methylation data can provide valuable information, when evaluatedindependently or in combination with other information such as genotypeor gene expression patterns. One object of the methods set forth hereinis to determine this information, e.g. if one or more sites in a genomeare differentially methylated in a test sample compared to a referencesample or data set.

Particular embodiments can be used for the detection, screening,monitoring (e.g. for relapse, remission, or response to treatment),staging, classification (e.g. for aid in choosing the most appropriatetreatment modality) and prognostication of cancer using methylationanalysis of circulating plasma/serum DNA.

Cancer DNA is known to demonstrate aberrant DNA methylation (see, forexample, Herman et al. 2003 N Engl J Med 349: 2042-2054, which isincorporated herein by reference). For example, the CpG site promotersof genes, e.g. tumor suppressor genes, are hypermethylated while the CpGsites in the gene body are hypomethylated when compared with non-cancercells. In particular embodiments of the methods set forth herein, amethylation pattern detected from the blood of an individual suspectedof having cancer is indicative of the methylation state of potentiallycancerous tissues such that the pattern is expected to be differentbetween individuals with cancer when compared with those healthyindividuals without cancer or when compared with those whose cancer hasbeen cured.

Because aberrant methylation occurs in most cancers, the methodsdescribed herein can be applied to the detection of any of a variety ofmalignancies with aberrant methylation, for example, malignancies inlung, breast, colorectum, prostate, nasopharynx, stomach, testes, skin,nervous system, bone, ovary, liver, hematologic tissues, pancreas,uterus, kidney, lymphoid tissues, etc. The malignancies may be of avariety of histological subtypes, for example, carcinomas,adenocarcinomas, sarcomas, fibroadenocarcinoma, neuroendocrine, orundifferentiated.

In particular embodiments, a method for determining methylation patternscan be used to monitor development of a fetus (e.g. to determine thepresence or absence of a developmental abnormality) or to determine thepresence of a particular disease or condition. In such cases the methodcan be carried out using a sample (e.g. blood, tissue or amniotic fluid)obtained from a pregnant female and the sample can be evaluated formethylation levels of fetal nucleic acids. A DNA methylation profile ofplacental tissues can be used to evaluate the pathophysiology ofpregnancy-associated or developmentally-related diseases, such aspreeclampsia and intrauterine growth restriction. Disorders in genomicimprinting are associated with developmental disorders, such asPrader-Willi syndrome and Angelman syndrome, and can be identified orevaluated using methods of the present disclosure. Altered profiles ofgenomic imprinting and global DNA methylation in placental and fetaltissues have been observed in pregnancies resulting from assistedreproductive techniques (see, for example, Hiura et al. 2012 Hum Reprod;27: 2541-2548, incorporated herein by reference) and can be detectedusing methods set forth herein. Exemplary methods that can be modifiedfor use with the methods of the present disclosure are forth in US Pat.App. Pub. Nos. 2013/0189684 A1 or 2014/0080715 A1, each of which isincorporated herein by reference.

The ability to determine placental or fetal methylation patterns frommaternal plasma provides a noninvasive method to determine, detect andmonitor pregnancy-associated conditions such as preeclampsia,intrauterine growth restriction, preterm labor and others. For example,the detection of a disease-specific aberrant methylation signatureallows the screening, diagnosis and monitoring of suchpregnancy-associated conditions.

Additionally, a method set forth herein to obtain diagnostic orprognostic information for other conditions. For example, liver tissuecan be analyzed to determine a methylation pattern specific to theliver, which may be used to identify liver pathologies. Other tissueswhich can also be analyzed include brain cells, bones, the lungs, theheart, the muscles and the kidneys, etc. DNA can be obtained from bloodsamples and analyzed in a method set forth herein in order to determinethe state of any of a variety of tissues that contribute DNA to theblood.

Furthermore, methylation patterns of transplanted organs can bedetermined from plasma DNA of organ transplantation recipients.Transplant analysis from plasma, can be a synergistic technology totransplant genomic analysis from plasma, such as technology set forth inZheng at al. 2012 Clin Chem 58: 549-558; Lo at al. 1998 Lancet 351:1329-1330; or Snyder et al. 2011 Proc Natl Acad Sci USA; 108: 6229-6234,each of which is incorporated herein by reference.

The methylation patterns of various tissues may change from time totime, e.g. as a result of development, aging, disease progression (e.g.inflammation, cancer or cirrhosis) or treatment. The dynamic nature ofDNA methylation makes such analysis potentially very valuable formonitoring of physiological and pathological processes. For example, ifone detects a change in the plasma methylation pattern of an individualcompared to a baseline value obtained when they were healthy, one couldthen detect disease processes in organs that contribute plasma DNA.

Terms used herein will be understood to take on their ordinary meaningin the relevant art unless specified otherwise. Several terms usedherein and their meanings are set forth below.

As used herein, the term “cell-free,” when used in reference to DNA, isintended to mean DNA that has been removed from a cell in vivo. Theremoval of the DNA can be a natural process such as necrosis orapoptosis. Cell-free DNA is generally obtained from blood, or a fractionthereof, such as plasma. Cell-free DNA can be obtained from other bodilyfluids or tissues.

As used herein, the term “cell type” is intended to identify cells basedon morphology, phenotype, developmental origin or other known orrecognizable distinguishing cellular characteristic. A variety ofdifferent cell types can be obtained from a single organism (or from thesame species of organism). Exemplary cell types include, but are notlimited to urinary bladder, pancreatic epithelial, pancreatic alpha,pancreatic beta, pancreatic endothelial, bone marrow lymphoblast, bonemarrow B lymphoblast, bone marrow macrophage, bone marrow erythroblast,bone marrow dendritic, bone marrow adipocyte, bone marrow osteocyte,bone marrow chondrocyte, promyeloblast, bone marrow megakaryoblast,bladder, brain B lymphocyte, brain glial, neuron, brain astrocyte,neuroectoderm, brain macrophage, brain microglia, brain epithelial,cortical neuron, brain fibroblast, breast epithelial, colon epithelial,colon B lymphocyte, mammary epithelial, mammary myoepithelial, mammaryfibroblast, colon enterocyte, cervix epithelial, ovary epithelial, ovaryfibroblast, breast duct epithelial, tongue epithelial, tonsil dendritic,tonsil B lymphocyte, peripheral blood lymphoblast, peripheral blood Tlymphoblast, peripheral blood cutaneous T lymphocyte, peripheral bloodnatural killer, peripheral blood B lymphoblast, peripheral bloodmonocyte, peripheral blood myeloblast, peripheral blood monoblast,peripheral blood promyeloblast, peripheral blood macrophage, peripheralblood basophil, liver endothelial, liver mast, liver epithelial, liver Blymphocyte, spleen endothelial, spleen epithelial, spleen B lymphocyte,liver hepatocyte, liver Alexander, liver fibroblast, lung epithelial,bronchus epithelial, lung fibroblast, lung B lymphocyte, lung Schwann,lung squamous, lung macrophage, lung osteoblast, neuroendocrine, lungalveolar, stomach epithelial and stomach fibroblast. In someembodiments, two cells can be considered to be the same type of celldespite one of the cells having been phenotypically or morphologicallyaltered by a condition or disease such as cancer. For purposes ofcomparison, a first cell that has been altered by a disease or conditioncan be compared to a second cell based on the known or suspected stateof the first cell prior to having been altered. For example, a cancerouspancreatic ductal epithelium cell can be considered to be the same typeof cell as a non-cancerous pancreatic ductal epithelium cell.

As used herein, the term “circulating,” when used in reference to DNA,is intended to mean DNA that is or was moving through the circulatorysystem of an organism, whether in cell-free form or inside circulatingcells.

As used herein, the term “coverage,” when used in reference to a geneticlocus, is intended to mean the number of detection events (e.g. sequencereads) that align to, or “cover,” the locus. In some embodiments, theterm refers to the average number of detection events (e.g. sequencereads) that align to, or “cover,” a plurality of loci. Generally, thecoverage level obtained from a sequencing method correlates directlywith the degree of confidence in the accuracy of the call (e.g.nucleotide type or methylation state) determined at a particular baseposition or genetic locus. At higher levels of coverage, a locus iscovered by a greater number of aligned sequence reads, so calls can bemade with a higher degree of confidence.

As used herein, the term “CpG site” is intended to mean the location ina nucleic acid molecule, or sequence representation of the molecule,where a cytosine nucleotide and guanine nucleotide occur, the 3′ oxygenof the cytosine nucleotide being covalently attached to the 5′ phosphateof the guanine nucleotide. The nucleic acid is typically DNA. Thecytosine nucleotide can optionally contain a methyl moiety,hydroxymethyl moiety or hydrogen moiety at position 5 of the pyrimidinering.

As used herein, the term “derived,” when used in reference to DNA, isintended to refer to the source from which the DNA was obtained or theorigin where the DNA was synthesized. In the case of biologicallyderived DNA, the term can be used to refer to an in vivo source fromwhich the DNA was obtained or the in vivo origin where the DNA wassynthesized. Exemplary origins include, but are not limited to, a cell,cell type, tissue, tissue type, organism or species of organism. In thecase of synthetically derived DNA, the term can be used to refer to anin vitro source from which the DNA was obtained or the in vitro originwhere the DNA was synthesized. A DNA molecule that is derived from aparticular source or origin can nonetheless be subsequently copied oramplified. The sequence of the resulting copies or amplicons can bereferred to as having been derived from the source or origin.

As used herein, the term “each,” when used in reference to a collectionof items, is intended to identify an individual item in the collectionbut does not necessarily refer to every item in the collection.Exceptions can occur if explicit disclosure or context clearly dictatesotherwise.

As used herein, the term “methylation difference” is intended to mean aqualitative or quantitative indicia that two nucleotides or nucleicacids do not have the same methylation state. The methylation differencecan be indicated for nucleotides that are at aligned positions ondifferent nucleic acids. In some cases the methylation difference can bea sum or aggregate of a plurality of aligned positions. When two or morenucleic acids are aligned, the methylation difference can be an averageacross one or more aligned positions.

As used herein, the term “methylation state,” when used in reference toa locus (e.g., a CpG site or polynucleotide segment) across severalmolecules having that locus, refers to one or more characteristics ofthe locus relevant to presence or absence of a methyl moiety.Non-limiting examples of such characteristics include whether any of thecytosine (C) bases within a locus are methylated, location of methylatedC base(s), percentage of methylated C base(s) at a particular locus, andallelic differences in methylation due to, for example, difference inthe origin of alleles. Reference to the methylation state of aparticular CpG site in a nucleic acid molecule, is directed to thepresence or absence of a methyl moiety at position 5 of the pyrimidinering of a cytosine. The term can be applied to one or more cytosinenucleotides (or representations thereof e.g. a chemical formula), or toone or more nucleic acid molecules (or representations thereof e.g. asequence representation). The term can also refer to the relative orabsolute amount (e.g., concentration) of methylated C or non-methylatedC at a particular locus in a nucleic acid. A methylation state sometimesis hypermethylated and sometimes is hypomethylated. For example, if allor a majority of C bases within a locus are methylated, the methylationstate can be referred to as “hypermethylated.” In another example, ifall or a majority of C bases within a locus are not methylated, themethylation state may be referred to as “hypomethylated.” Likewise, ifall or a majority of C bases within a locus are methylated as comparedto reference then the methylation state is considered hypermethylatedcompared to the reference. Alternatively, if all or a majority of the Cbases within a locus are not methylated as compared to a reference thenthe methylation state is considered hypomethylated compared to thereference.

A “methylation site” is a locus in a nucleic acid where methylation hasoccurred, or has the possibility of occurring. A methylation sitesometimes is a C base, or multiple C bases in a region, and sometimes amethylation site is a CpG site in a locus. Each methylation site in thelocus may or may not be methylated. A methylation site can besusceptible to methylation by a naturally occurring event in vivo or byan event that chemically methylates a nucleotide in vitro.

As used herein, the term “mixture,” when used in reference to two ormore components, is intended to mean that the two or more components aresimultaneously present in a fluid or vessel. The components aretypically capable of contacting each other via diffusion or agitation.The components may be separate molecules (e.g. two or more nucleic acidfragments) or the components may be part of a single molecule (e.g.sequence regions on a long nucleic acid molecule).

As used herein, the term “tissue” is intended to mean a collection oraggregation of cells that act together to perform one or more specificfunctions in an organism. The cells can optionally be morphologicallysimilar. Exemplary tissues include, but are not limited to, eye, muscle,skin, tendon, vein, artery, blood, heart, spleen, lymph node, bone, bonemarrow, lung, bronchi, trachea, gut, small intestine, large intestine,colon, rectum, salivary gland, tongue, gall bladder, appendix, liver,pancreas, brain, stomach, skin, kidney, ureter, bladder, urethra, gonad,testicle, ovary, uterus, fallopian tube, thymus, pituitary, thyroid,adrenal, or parathyroid. Tissue can be derived from any of a variety oforgans of a human or other body.

The embodiments set forth below and recited in the claims can beunderstood in view of the above definitions.

The present disclosure provides a method for distinguishing an aberrantmethylation level for DNA from a first cell type. The method can includesteps of (a) providing a test data set that includes (i) methylationstates for a plurality of sites (e.g. CpG sites) from test genomic DNAfrom at least one test organism, and (ii) coverage at each of the sites(e.g. CpG sites) for detection of the methylation states; (b) providingmethylation states for the plurality of sites (e.g. CpG sites) inreference genomic DNA from one or more reference individual organisms,(c) determining, for each of the sites (e.g. CpG sites), the methylationdifference between the test genomic DNA and the reference genomic DNA,thereby providing a normalized methylation difference for each sites(e.g. CpG sites); and (d) weighting the normalized methylationdifference for each sites (e.g. CpG sites) by the coverage at each ofthe sites (e.g. CpG sites), thereby determining an aggregatecoverage-weighted normalized methylation difference score. Optionallythe sites from the test genomic DNA are derived from a plurality ofdifferent cell types from the individual test organism and as a furtheroption the cell type from which each of the sites is derived is unknown.In a further optional embodiment, the individual test organism and theone or more reference individual organisms are the same species.

Also provided is a method for distinguishing an aberrant methylationlevel for DNA from a sample containing DNA from a plurality of differentcell types, including steps of (a) providing a sample containing amixture of genomic DNA from a plurality of different cell types from atleast one test organism, thereby providing test genomic DNA; (b)detecting methylation states for a plurality of sites (e.g. CpG sites)in the test genomic DNA; (c) determining the coverage at each of thesites (e.g. CpG sites) for the detecting of the methylation states; (d)providing methylation states for the plurality of sites (e.g. CpG sites)in reference genomic DNA from at least one reference individual, the atleast one test organism and reference individual optionally being thesame species; (e) determining, for each of the sites (e.g. CpG sites),the methylation difference between the test genomic DNA and thereference genomic DNA, thereby providing a normalized methylationdifference for each site (e.g. CpG site); and (f) weighting thenormalized methylation difference for each site (e.g. CpG site) by thecoverage at each of the sites (e.g. CpG sites), thereby determining anaggregate coverage-weighted normalized methylation difference score.

The present invention also provides an alternative sensitive method fordistinguishing an aberrant methylation level for DNA from a first celltype.

The first stage of this method involves establishing a methylationbaseline, including the steps of (a) providing methylation states for aplurality of sites (e.g., CpG sites) in baseline genomic DNA from two ormore normal individual organisms; and (b) determining, for each of thesites (e.g., CpG sites), the mean methylation level and standarddeviation of methylation levels for the baseline genomic DNA. In someembodiments, the number of normal individual organisms providingbaseline genomic DNA is at least 3, at least 5, at least 10, at least20, at least 50, or at least 100.

The second stage of this method involves determining aggregatemethylation scores for a plurality of training samples, including thesteps of (c) providing a training set of normal genomic DNA samples fromtwo or more normal individual organisms that includes (i) methylationstates for a plurality of sites (e.g., CpG sites) in the training set ofnormal genomic DNA samples, and optionally (ii) coverage at each of thesites (e.g., CpG sites) for detection of the methylation states; (d)determining, for each of the sites (e.g., CpG sites), the methylationdifference between each normal genomic DNA sample of the training setand the baseline genomic DNA, thereby providing a normalized methylationdifference for each normal genomic DNA sample of the training set ateach site (e.g., CpG site); (e) converting the normalized methylationdifference for each normal genomic DNA sample of the training set ateach site (e.g., CpG site) into the probability of observing such anormalized methylation difference or greater (e.g., a one-sidedp-value), and optionally weighting the probability of such an event; (f)determining an aggregate methylation score for each normal genomic DNAsample of the training set to obtain training set methylation scores;and (g) calculating the mean methylation score and standard deviation ofthe training set methylation scores. In some embodiments, the number ofnormal individual organisms providing genomic DNA for the training setis at least 3, at least 5, at least 10, at least 20, at least 50, or atleast 100.

The third stage of this method, which can be carried out before, after,or concurrently with the second stage, involves determining an aggregatemethylation score for a given test sample, including the steps of (h)providing a test data set that includes (i) methylation states for theplurality of sites (e.g., CpG sites) from test genomic DNA from at leastone test organism, and optionally (ii) coverage at each of the sites(e.g., CpG sites) for detection of the methylation states; (i)determining, for each of the sites (e.g., CpG sites), the methylationdifference between the test genomic DNA and the baseline genomic DNA,thereby providing a normalized methylation difference for the testgenomic DNA; (j) converting the normalized methylation difference forthe test genomic DNA at each of the sites (e.g., CpG sites) into theprobability of observing such a normalized methylation difference orgreater (e.g., a one-sided p-value), and optionally weighting theprobability of such an event; and (k) determining an aggregatemethylation score for the test genomic DNA.

The fourth and final stage of this method involves the step of (1)comparing the methylation score of the test genomic DNA to the meanmethylation score and standard deviation of methylation scores in thetraining set of normal genomic DNA to determine the number of standarddeviations the methylation score of the test genomic DNA is from thedistribution of methylation scores in the training set of normal genomicDNA. In the event the number of standard deviations exceeds apredetermined threshold value (e.g., 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0,etc.), the test sample is considered to have an aberrant DNA methylationlevel.

Optionally, the methylation sites (e.g., CpG sites) from the testgenomic DNA are derived from a plurality of different cell types fromthe individual test organism, and as a further option, the cell typefrom which each of the sites (e.g., CpG sites) is derived is unknown. Ina further optional embodiment, the individual test organism and the oneor more baseline individual organisms, training individual organisms, ora combination thereof are the same species.

Also provided is an alternative sensitive method for distinguishing anaberrant methylation level for DNA from a sample containing DNA from aplurality of different cell types.

The first stage of this method involves establishing a methylationbaseline, including the steps of (a) providing methylation states for aplurality of sites (e.g., CpG sites) in baseline genomic DNA from two ormore normal individual organisms; and (b) determining, for each of thesites (e.g., CpG sites), the mean methylation level and standarddeviation of methylation levels for the baseline genomic DNA. In someembodiments, the number of normal individual organisms providingbaseline genomic DNA is at least 3, at least 5, at least 10, at least20, at least 50, or at least 100.

The second stage of this method involves determining aggregatemethylation scores for a plurality of training samples, including thesteps of (c) providing a training set of normal genomic DNA samples fromtwo or more normal individual organisms that includes (i) methylationstates for a plurality of sites (e.g., CpG sites) in the training set ofnormal genomic DNA samples, and optionally (ii) coverage at each of thesites (e.g., CpG sites) for detection of the methylation states; (d)determining, for each of the sites (e.g., CpG sites), the methylationdifference between each normal genomic DNA sample of the training setand the baseline genomic DNA, thereby providing a normalized methylationdifference for each normal genomic DNA sample of the training set ateach site (e.g., CpG site); (e) converting the normalized methylationdifference for each normal genomic DNA sample of the training set ateach site (e.g., CpG sites) into the probability of observing such anormalized methylation difference or greater (e.g., a one-sidedp-value), and optionally weighting the probability; (f) determining anaggregate methylation score for each normal genomic DNA sample of thetraining set to obtain training set methylation scores; and (g)calculating the mean methylation score and standard deviation of thetraining set methylation scores. In some embodiments, the number ofnormal individual organisms providing genomic DNA for the training setis at least 3, at least 5, at least 10, at least 20, at least 50, or atleast 100.

The third stage of this method, which can be carried out before, after,or concurrently with the second stage, involves determining an aggregatemethylation score for a given test sample, including the steps of (h)providing a mixture of genomic DNA from a test organism suspected ofhaving a condition associated with an aberrant DNA methylation level(e.g., cancer), wherein the mixture comprises genomic DNA from aplurality of different cell types from the test organism, therebyproviding test genomic DNA; (i) detecting methylation states for theplurality of sites (e.g., CpG sites) in the test genomic DNA, andoptionally determining the coverage at each of the sites (e.g., CpGsites) for the detecting of the methylation states; (j) determining, foreach of the sites (e.g., CpG sites), the methylation difference betweenthe test genomic DNA and the baseline genomic DNA, thereby providing anormalized methylation difference for the test genomic DNA; (k)converting the normalized methylation difference for the test genomicDNA at each of the sites (e.g., CpG sites) into the probability ofobserving such a normalized methylation difference or greater (e.g., aone-sided p-value), and optionally weighting the probability of such anevent; and (l) determining an aggregate methylation score for the testgenomic DNA.

The fourth and final stage of this method involves the step of (m)comparing the methylation score of the test genomic DNA to the meanmethylation score and standard deviation of methylation scores in thetraining set of normal genomic DNA to determine the number of standarddeviations the methylation score of the test genomic DNA is from thedistribution of methylation scores in the training set of normal genomicDNA. In the event the number of standard deviations exceeds apredetermined threshold value (e.g., 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5,5.0, etc.), the test sample is considered to have an aberrant DNAmethylation level.

A method set forth herein can be carried out for any of a variety oftest organisms. Exemplary organisms include, without limitation,eukaryotic (unicellular or multicellular) organisms. Exemplaryeukaryotic organisms include a mammal such as a rodent, mouse, rat,rabbit, guinea pig, ungulate, horse, sheep, pig, goat, cow, cat, dog,primate, human or non-human primate; a plant such as Arabidopsisthaliana, corn (Zea mays), sorghum, oat (Oryza sativa), wheat, rice,canola, or soybean; an algae such as Chlamydomonas reinhardtii; anematode such as Caenorhabditis elegans; an insect such as Drosophilamelanogaster, mosquito, fruit fly, honey bee or spider; a fish such aszebrafish (Danio rerio); a reptile: an amphibian such as a frog orXenopus laevis; a Dictyostelium discoideum; a fungi such as Pneumocystiscarinii, Takifugu rubripes, yeast such as Saccharomyces cerevisiae orSchizosaccharomyces pombe; or a Plasmodium falciparum. A method of thepresent disclosure can also be used to evaluate methylation in organismssuch as prokaryotes, examples of which include a bacterium, Escherichiacoli, Staphylococci or Mycoplasma pneumoniae; an archae; a virus,examples of which include Hepatitis C virus or human immunodeficiencyvirus; or a viroid.

Particular embodiments of the methods set forth herein can provideadvantages when applied to multicellular organisms because the methodsprovide for determination of the methylation states for genomic DNAderived from a particular cell or tissue in a background of nucleicacids derived from other cells or tissues. Thus, the methods set forthherein can be particularly useful for mammals, such as humans. In somecases the methods can be carried out on samples containing nucleic acidmixtures from several different cell types or tissue types such assamples obtained from the blood or other biological fluid of amulticellular organism. Furthermore, the methods set forth herein can beadvantageously employed for evaluation of methylation states for genomicDNA obtained from somatic cells of a pregnant female mammal, such as apregnant female human, and/or the methylation states for genomic DNAobtained from somatic cells of one or more prenatal offspring carried bythe female.

In some embodiments, the methods can be carried out for a mixture ofgenomic DNA from several different cell types from a mixed organismenvironment (e.g. metagenomics sample) such as an ecological sample(e.g. pond, ocean, thermal vent, etc.) or digestive system sample (e.g.mouth, gut, colon, etc.). Thus, the method can be carried out for amixed organism sample wherein individual species are not separated orcultivated.

As will be evident from several exemplary embodiments set forth herein,the CpG sites from a test genomic DNA that are evaluated in a method ofthis disclosure can optionally be derived from a plurality of differentcell types from the individual test organism. As a further option thecell type from which each of the CpG sites is derived need not be known.This will often be the case when the sample used in the method isderived from blood or another biological fluid or metagenomics sample.

In particular embodiments, the test sample used in a method set forthherein can include circulating tumor DNA and circulating non-tumor DNA.This can be the case when the test sample includes DNA obtained fromblood, for example, from an individual known or suspected to havecancer.

Particular embodiments of the methods set forth herein can be carriedout using methylation states for a plurality of sites from test genomicDNA from an individual test organism. In some cases the data is providedto an individual or system that carries out the method. Alternatively,embodiments of the methods can include one or more steps for detectingmethylation states for a plurality of sites in a test genome.

Methylation of sites, such as CpG dinucleotide sequences, can bemeasured using any of a variety of techniques used in the art for theanalysis of such sites. For example, methylation can be measured byemploying a restriction enzyme based technology, which utilizesmethylation sensitive restriction endonucleases for the differentiationbetween methylated and unmethylated cytosines. Restriction enzyme basedtechnologies include, for example, restriction digest withmethylation-sensitive restriction enzymes followed by nucleic acidsequencing (e.g. massively parallel or Next Generation sequencing),Southern blot analysis, real time PCR, restriction landmark genomicscanning (RLGS) or differential methylation hybridization (DMH).

Restriction enzymes characteristically hydrolyze DNA at and/or uponrecognition of specific sequences or recognition motifs that aretypically between 4- to 8-bases in length. Among such enzymes,methylation sensitive restriction enzymes are distinguished by the factthat they either cleave, or fail to cleave DNA according to the cytosinemethylation state present in the recognition motif, in particular, ofthe CpG sequences. In methods employing such methylation sensitiverestriction enzymes, the digested DNA fragments can be differentiallyseparated (e.g. based on size or hybridization affinity to complementaryprobes), differentially amplified (e.g. based on affinity to anamplification primer), or differentially detected (e.g. via a microarraydetection technique or nucleic acid sequencing technique) such that themethylation status of the sequence can thereby be deduced.

In some embodiments that employ methylation sensitive restrictionenzymes, a post-digest PCR amplification step is added wherein a set oftwo oligonucleotide primers, one on each side of the methylationsensitive restriction site, is used to amplify the digested genomic DNA.PCR products are produced and detected for templates that were notrestricted (e.g. due to presence of a methylated restriction site)whereas PCR products are not produced where digestion of the subtendedmethylation sensitive restriction enzyme site occurs. Techniques forrestriction enzyme based analysis of genomic methylation are well knownin the art and include the following: differential methylationhybridization (DMH) (Huang et al., 1999, Human Mol. Genet. 8, 459-70);Not I-based differential methylation hybridization (for example,WO02/086163A1); restriction landmark genomic scanning (RLGS) (Plass etal., 1999, Genomics 58:254-62); methylation sensitive arbitrarily primedPCR (AP-PCR) (Gonzalgo et al., 1997, Cancer Res. 57: 594-599);methylated CpG site amplification (MCA) (Toyota et. al., 1999, CancerRes. 59: 2307-2312). Other useful methods for detecting genomicmethylation are described, for example, in US Patent Applicationpublication 2003/0170684 A1 or WO 04/05122. The references cited aboveare incorporated herein by reference.

Methylation of CpG dinucleotide sequences can also be measured byemploying cytosine conversion based technologies, which rely onmethylation status-dependent chemical modification of CpG sequenceswithin isolated genomic DNA, or fragments thereof, followed by DNAsequence analysis. Chemical reagents that are able to distinguishbetween methylated and non-methylated CpG dinucleotide sequences includehydrazine, which cleaves the nucleic acid, and bisulfite. Bisulfitetreatment followed by alkaline hydrolysis specifically convertsnon-methylated cytosine to uracil, leaving 5-methylcytosine unmodifiedas described by Olek A., 1996, Nucleic Acids Res. 24:5064-6 or Frommeret al., 1992, Proc. Natl. Acad. Sci. USA 89:1827-1831, each of which isincorporated herein by reference. The bisulfite-treated DNA cansubsequently be analyzed by molecular techniques, such as PCRamplification, sequencing, and detection comprising oligonucleotidehybridization (e.g. using nucleic acid microarrays).

Techniques for the analysis of bisulfite treated DNA can employmethylation-sensitive primers for the analysis of CpG methylation statuswith isolated genomic DNA, for example, as described by Herman et al.,1996, Proc. Natl. Acad. Sci. USA 93:9821-9826, or U.S. Pat. Nos.5,786,146 or 6,265,171, each of which is incorporated herein byreference. Methylation sensitive PCR (MSP) allows for the detection of aspecific methylated CpG position within, for example, the regulatoryregion of a gene. The DNA of interest is treated such that methylatedand non-methylated cytosines are differentially modified, for example,by bisulfite treatment, in a manner discernable by their hybridizationbehavior. PCR primers specific to each of the methylated andnon-methylated states of the DNA are used in PCR amplification. Productsof the amplification reaction are then detected, allowing for thededuction of the methylation status of the CpG position within thegenomic DNA. Other methods for the analysis of bisulfite treated DNAinclude methylation-sensitive single nucleotide primer extension(Ms-SNuPE) (see, for example, Gonzalgo & Jones, 1997; Nucleic Acids Res.25:2529-2531, or U.S. Pat. No. 6,251,594, each of which is incorporatedherein by reference), or the use of real time PCR based methods, such asthe art-recognized fluorescence-based real-time PCR techniqueMethyLight™ (see, for example, Eads et al., 1999; Cancer Res.59:2302-2306, U.S. Pat. No. 6,331,393 or Heid et al., 1996, Genome Res.6:986-994, each of which is incorporated herein by reference). It willbe understood that a variety of methylation assay methods can be usedfor the determination of the methylation status of particular genomicCpG positions. Methods which employ bisulfite conversion include, forexample, bisulfite sequencing, methylation-specific PCR,methylation-sensitive single nucleotide primer extension (Ms-SnuPE),MALDI mass spectrometry and methylation-specific oligonucleotide arrays,for example, as described in U.S. Pat. No. 7,611,869 or InternationalPatent Application WO2004/051224, each of which is incorporated hereinby reference.

In particular embodiments, methylation of genomic CpG positions in asample can be detected using an array of probes. In such embodiments, aplurality of different probe molecules can be attached to a substrate orotherwise spatially distinguished in an array. Exemplary arrays that canbe used in the invention include, without limitation, slide arrays,silicon wafer arrays, liquid arrays, bead-based arrays and others knownin the art or set forth in further detail herein. In preferredembodiments, the methods of the invention can be practiced with arraytechnology that combines a miniaturized array platform, a high level ofassay multiplexing, and scalable automation for sample handling and dataprocessing. Particularly useful arrays are described in U.S. Pat. Nos.6,355,431; 6,429,027; 6,890,741; 6,913,884 or 7,582,420; or U.S. Pat.App. Pub. Nos. 2002/0102578 A1; 2005/0053980 A1; 2005/0181440 A1; or2009/0186349 A1, each of which is incorporated herein by reference.Further examples of useful arrays include those described in U.S. Pat.Nos. 6,023,540, 6,200,737 or 6,327,410; or PCT Pub. Nos. WO9840726,WO9918434 or WO9850782, each of which is incorporated herein byreference.

The plexity of an array used in the invention can vary depending on theprobe composition and desired use of the array. For example, the plexityof nucleic acids (or CpG sites) detected in an array can be at least 10,100, 1,000, 10,000, 0.1 million, 1 million, 10 million, 100 million ormore. Alternatively or additionally, the plexity can be selected to beno more than 100 million, 10 million, 1 million, 0.1 million, 10,000,1,000, 100 or less. Of course, the plexity can be between one of thelower values and one of the upper values selected from the ranges above.Similar plexitiy ranges can be achieved using nucleic acid sequencingapproaches such as those known in the art as Next Generation ormassively parallel sequencing.

A variety of commercially available array-based products for detectionof methylation can be used including, for example, the MethylationEPIC™BeadChip™(Illumina, Inc., San Diego, Calif.) which allows interrogationof over 850,000 methylation sites quantitatively across the human genomeat single-nucleotide resolution. Also useful are methylation microarraysavailable from Agilent (Santa Clara, Calif.) and other commercialsuppliers of nucleic acid arrays. The array products can be customizedfor detection of a wide variety of methylation sites in the human genomeor other genomes.

Detection of one or more nucleic acids obtained or generated in atechnique set forth herein can employ a sequencing procedure, such as asequencing-by-synthesis (SBS) technique or other techniques known in theart as massively parallel sequencing or Next Generation sequencing.Briefly, SBS can be initiated by contacting the target nucleic acidswith one or more labeled nucleotides, DNA polymerase, etc. The targetnucleic acid can be derived from a methylation detection technique suchas bisulfate conversion or restriction with a methyl sensitiverestriction endonuclease. Those features where a primer is extendedusing the target nucleic acid as template will incorporate a labelednucleotide that can be detected. Optionally, the labeled nucleotides canfurther include a reversible termination property that terminatesfurther primer extension once a nucleotide has been added to a primer.For example, a nucleotide analog having a reversible terminator moietycan be added to a primer such that subsequent extension cannot occuruntil a deblocking agent is delivered to remove the moiety. Thus, forembodiments that use reversible termination, a deblocking reagent can bedelivered to the flow cell (before or after detection occurs). Washescan be carried out between the various delivery steps. The cycle canthen be repeated n times to extend the primer by n nucleotides, therebydetecting a sequence of length n. Exemplary SBS procedures, fluidicsystems and detection platforms that can be readily adapted for use witha method of the present disclosure are described, for example, inBentley et al., Nature 456:53-59 (2008), WO 04/018497; WO 91/06678; WO07/123744; U.S. Pat. Nos. 7,057,026; 7,329,492; 7,211,414; 7,315,019 or7,405,281, or US Pat. App. Pub. No. 2008/0108082 A1, each of which isincorporated herein by reference.

Other sequencing procedures that detect large numbers of nucleic acidsin parallel can be used, such as pyrosequencing. Pyrosequencing detectsthe release of inorganic pyrophosphate (PPi) as particular nucleotidesare incorporated into a nascent nucleic acid strand (Ronaghi, et al.,Analytical Biochemistry 242(1), 84-9 (1996); Ronaghi, Genome Res. 11(1),3-11 (2001); Ronaghi et al. Science 281(5375), 363 (1998); or U.S. Pat.Nos. 6,210,891; 6,258,568 or 6,274,320, each of which is incorporatedherein by reference). Sequencing-by-ligation reactions are also usefulincluding, for example, those described in Shendure et al. Science309:1728-1732 (2005); or U.S. Pat. Nos. 5,599,675 or 5,750,341, each ofwhich is incorporated herein by reference. Some embodiments can includesequencing-by-hybridization procedures as described, for example, inBains et al., Journal of Theoretical Biology 135(3), 303-7 (1988);Drmanac et al., Nature Biotechnology 16, 54-58 (1998); Fodor et al.,Science 251(4995), 767-773 (1995); or WO 1989/10977, each of which isincorporated herein by reference. Techniques that use fluorescenceresonance energy transfer (FRET) and/or zeromode waveguides can be usedsuch as those described in Levene et al. Science 299, 682-686 (2003);Lundquist et al. Opt. Lett. 33, 1026-1028 (2008); or Korlach et al.Proc. Natl. Acad. Sci. USA 105, 1176-1181 (2008), the disclosures ofwhich are incorporated herein by reference. Also useful are sequencingtechniques that employ detection of a proton released upon incorporationof a nucleotide into an extension product, such as those commerciallyavailable from Ion Torrent (Guilford, Conn., a Life Technologiessubsidiary) or described in US Pat. App. Pub. Nos. 2009/0026082 A1;2009/0127589 A1; 2010/0137143 A1; or 2010/0282617 A1, each of which isincorporated herein by reference.

Particularly useful sequencing platforms that can be employed includethose commercially available from Illumina, Inc. (San Diego, Calif.)such as the MiSeq™, NextSeq™ or HiSeq™ lines of nucleic acid sequencers;the 454 sequencing systems commercially available from Roche LifeSciences (Basel, Switzerland); the Ion Torrent sequencing systemsavailable from Life Technologies, a subsidiary of Thermo FisherScientific (Waltham, Mass.); or the nanopore sequencing systemscommercially available from Oxford Nanopore (Oxford, England). TheTruSeq™ DNA Methylation Kit is available from Illumina, Inc. and can beused to produce bisulfite sequencing libraries that can be detected onIllumina sequencers. Useful commercial products for preparing nucleicacid samples for detection of methylation on sequencing platforms fromIllumina or other suppliers include, for example, Methylation AnalysisSample Prep Products available from Thermo Fisher Scientific (Waltham,Mass.), Accel-NGS® Methyl-Seq DNA Library Kit (Swift Biosciences, AnnArbor, Mich.), EpiMark® Methylated DNA Enrichment Kit available from NewEngland BioLabs (Beverley, Mass.), the Pico Methyl-Seq™ Library Prep Kitavailable from Zymoresearch (Irvine, Calif.), or the Methylamp™Universal Methylated DNA Preparation Kit available from EpiGentek(Farmingdale, N.Y.).

Particular embodiments can include a step of manipulating a nucleic acidsample to enrich for desired nucleic acids. For example, a sample thatis provided for use in a method set forth herein can be subjected totargeted selection of a subset of genomic DNA fragments that include aset of predetermined target CpG sites. Targeted selection can occurprior to or after treating nucleic acids with bisulfite, methylsensitive endonucleases or other reagents used to distinguish methylatedsites from unmethylated sites. A useful targeted selection technique isset forth in Example I, below.

Particular embodiments of the methods set forth herein will evaluateand/or use the coverage determined for each of the sites wheremethylation states have been or will be determined. In some cases thecoverage data is provided to an individual or system that carries outthe method. Alternatively, embodiments of the methods can include one ormore steps for determining coverage at each of the sites.

For embodiments that detect methylation states via a sequencingtechnique, coverage can be considered to describe the average number ofsequencing reads that align to, or “cover,” particular sites (e.g. CpGsites). The Next Generation sequencing coverage level often determineswhether a particular sequence or site can be characterized with acertain degree of confidence. At higher levels of coverage, each site iscovered by a greater number of aligned sequence reads, socharacterizations can be made with a higher degree of confidence. Auseful guide for determining coverage is provided by Illumina TechnicalNote “Estimating Sequencing Coverage” Pub. No. 770-2011-022 (Dec. 1,2014), which is incorporated herein by reference. Similar coveragecriteria can be applied to other detection techniques besides NextGeneration sequencing techniques.

Particular embodiments of the present invention can use coverage that isat least 10×, 30×, 50×, 100×, 1,000×, 5,000×, 10,000× or more at eachsite. Alternatively or additionally, coverage can be at most 10,000×,5,000×, 1,000×, 100×, 50×, 30×, 10× or less. Coverage can be selectedbased on a desired confidence in determining methylation pattern takenin view of the number of sites being evaluated and the quantity of DNAused in the method.

As the number of sites evaluated increases, the confidence in thecharacterization of the sites will also increase. This means a lowercoverage can be acceptable. In particular embodiments the number ofsites evaluated can be at least 10 sites, 100 sites, 500 sites, 1×10³sites, 5×10³ sites, 1×10⁴ sites, 1×10⁵ sites, 1×10⁶ sites or more.Alternatively or additionally, the number of sites evaluated can be atmost 1×10⁶ sites, 1×10⁵ sites, 1×10⁴ sites, 1×10³ sites, 100 sites or 10sites.

The quantity of DNA used in a method set forth herein will depend uponseveral factors such as the sample used and the analytical steps carriedout on the sample. A typical blood draw will provide 30 ng ofcirculating DNA. However, larger or smaller quantities of DNA can beprovided by altering the volume of blood drawn, by using a differenttype of sample (such as those exemplified elsewhere herein) and/orutilizing sample extraction techniques with higher or lower yields.Accordingly, a method of the present invention can be carried out usinga quantity of DNA that is at least 3 ng, 10 ng, 30 ng, 50 ng, 100 ng,500 ng or more. Alternatively or additionally, the quantity of DNA canbe at most 500 ng, 100 ng, 50 ng, 30 ng, 10 ng or 3 ng.

Furthermore, in some embodiments the DNA used in a method for evaluatingmethylation states is a mixture of DNA from a target cell or tissue(e.g. tumor DNA) in a background of DNA from other cells or tissues(e.g. non-tumor DNA). The percent DNA from the target tissue or cell canbe at most 90%, 50%, 25%, 10%, 1%, 0.1%, 0.01% or lower. Alternativelyor additionally, the percent DNA from the target tissue or cell can beat least 0.01%, 0.1%, 1%, 10%, 25%, 50%, 90% or higher.

The above parameters of DNA amount, coverage, number of sites andpercent DNA from the target cell or tissue can be adjusted, for example,within the ranges exemplified above to accommodate a desired confidencelevel in characterizing methylation states for nucleic acids in a methodset forth herein.

Particular embodiments of the methods set forth herein include a step ofproviding methylation states for the plurality of sites in referencegenomic DNA from one or more reference individual organisms. Optionally,a method can include one or more steps for detecting the methylationstates for the plurality of sites in reference genomic DNA from one ormore reference individual organisms. In one aspect, a reference genomicDNA can include, for instance, baseline samples. Any one of the methodsset forth herein for determining methylation states of test DNA can beused to determine methylation states for reference DNA.

Reference genomic DNA, such as baseline samples, that is used in amethod of the present disclosure can be from one or more organism thatis (or are) the same species as the test organism. For example, when thetest organism is an individual human, the reference genomic DNA can befrom a different human individual. In some embodiments, the referencegenomic DNA is from the same individual who provided the test genomicDNA material. For example, the test DNA can be from a tissue suspectedof having a particular condition, whereas the reference DNA is from atissue that is known not to have the condition. In particularembodiments, the test DNA can be from a tumor sample obtained from anindividual whereas the reference DNA is from a normal tissue obtainedfrom the same individual. The tissue or cell types can be the same, butfor the fact that one of the tissue or cell types has a condition thatthe other tissue or cell type does not. Alternatively, different tissueor cell types can be obtained from the individual, one of the tissue orcell types providing test DNA and the other tissue or cell typeproviding reference DNA. A reference genomic DNA can be obtained from ametagenomics sample (e.g. environmental or community sample), forexample, to be used in comparison to a test metagenomics sample.

A test DNA can be derived from one or more test organisms at a differenttime from when a reference DNA, such as baseline samples, is derivedfrom the one or more test organisms. For example, a reference DNA samplecan be obtained from an individual at a time prior to when a disease orcondition is suspected to be present, and then a test DNA sample can beobtained from the individual at a later time when the individual issuspected of having a disease or condition. In such embodiments the testDNA and reference DNA can be obtained from similar tissues, communitiesor cell types or from different tissues, communities or cell types.

In one embodiment, a method of the present disclosure can include a stepof determining, for a plurality of sites (e.g. CpG sites), themethylation difference between test genomic DNA and reference genomicDNA, thereby providing a normalized methylation difference for each site(e.g. CpG site). In particular embodiments the normalized methylationdifference, also referred to as z-score, at a particular site (e.g., CpGsite) is determined according to the formula

$Z_{i} = \frac{\chi_{i} - \mu_{i}}{\sigma_{i}}$

wherein Z_(i) represents a normalized methylation difference for aparticular site identified as i, χ_(i) represents the methylation levelat site i in the test genomic DNA, μ_(i) represents the mean methylationlevel at site i in the reference genome, and σ_(i) represents thestandard deviation of methylation levels at site i in the referencegenomic DNA. Use of the formula for determining methylation differenceis exemplified in Example I, below.

A method of the present disclosure can further include a step ofweighting the normalized methylation difference for each site (e.g., CpGsite) by the coverage at each of the sites (e.g., CpG sites), therebydetermining an aggregate coverage-weighted normalized methylationdifference score. In particular embodiments, an aggregatecoverage-weighted normalized methylation difference score (representedas A) is determined according to the formula

$A = \frac{\sum_{i = 1}^{k}{w_{i}Z_{i}}}{\sqrt{\sum_{i = 1}^{k}w_{i}^{2}}}$

wherein w_(i) represents the coverage at site i, and k represents thetotal number of sites. Use of the formula for determining an aggregatecoverage-weighted normalized methylation difference score is exemplifiedin Example I, below.

In particular embodiments, the methods set forth herein can be used toidentify a change in methylation state for a test organism or to monitorsuch changes over time. Accordingly, the present disclosure provides amethod that includes steps of (a) providing a test data set thatincludes (i) methylation states for a plurality of sites from testgenomic DNA from at least one test organism, and (ii) coverage at eachof the sites for detection of the methylation states; (b) providingmethylation states for the plurality of sites in reference genomic DNAfrom one or more reference individual organisms, (c) determining, foreach of the sites, the methylation difference between the test genomicDNA and the reference genomic DNA, thereby providing a normalizedmethylation difference for each site; (d) weighting the normalizedmethylation difference for each site by the coverage at each of thesites, thereby determining an aggregate coverage-weighted normalizedmethylation difference score and (e) repeating steps (a) through (d)using a second test data set that includes (i) methylation states forthe plurality of sites from a second test genomic DNA from theindividual test organism, and (ii) coverage at each of the sites fordetection of the methylation states, and using the same referencegenomic DNA from the at least one reference individual, and (f)determining whether or not a change has occurred in the aggregatecoverage-weighted normalized methylation difference score between thetest genomic DNA and the second test genomic DNA.

Also provided is a method that includes the steps of (a) providing asample containing a mixture of genomic DNA from a plurality of differentcell types from at least one test organism, thereby providing testgenomic DNA; (b) detecting methylation states for a plurality of sitesin the test genomic DNA; (c) determining the coverage at each of thesites for the detecting of the methylation states; (d) providingmethylation states for the plurality of sites in reference genomic DNAfrom at least one reference individual, the at least one test organismand reference individual optionally being the same species; (e)determining, for each of the sites, the methylation difference betweenthe test genomic DNA and the reference genomic DNA, thereby providing anormalized methylation difference for each site; (f) weighting thenormalized methylation difference for each site by the coverage at eachof the sites, thereby determining an aggregate coverage-weightednormalized methylation difference score; (g) repeating steps (a) through(f) using a second test genomic DNA provided from a sample comprising amixture of genomic DNA from a plurality of different cell types from theat least one test organism, and using the same reference genomic DNAfrom the at least one reference individual, and (h) determining whetheror not a change has occurred in the aggregate coverage-weightednormalized methylation difference score between the test genomic DNA andthe second test genomic DNA.

In another embodiment, the method is refined to take into considerationthe observed variations in aggregate DNA methylation within a normalpopulation. The test genomic DNA is not compared directly to a referencegenomic DNA; rather, an intermediate step is interposed that includesthe evaluation of a training set of normal genomic DNA samples againstthe reference genomic DNA—referred to in this embodiment as baselinesamples—to assess variation of aggregate DNA methylation within a normalpopulation. This involves calculating “methylation scores” for eachmember of a training set of normal genomic DNA samples, and determiningthe mean and standard deviation of the methylation scores of thetraining set population, thereby yielding information about thedistribution of methylation scores in a normal population. In someembodiments, the number of normal individual organisms providing genomicDNA for the training set is at least 3, at least 5, at least 10, atleast 20, at least 50, or at least 100.

In this embodiment, the method can include a first step of determining,for each CpG site i, the mean methylation level (μ_(i)) and standarddeviation of methylation levels (σ_(i)), observed for a population ofreference genomic DNA. Here, the reference or baseline genomic DNA takesthe form of a population of normal genomic DNA samples. A selectedgenomic DNA can then be compared to the baseline DNA population toevaluate variation in methylation levels. More specifically, methylationlevels at each site i (e.g., CpG site) in a selected genomic DNA can becompared to the population mean, μ_(i), for the baseline samples togenerate a methylation score for the selected genomic DNA. In oneembodiment, the selected genomic DNA is a set of training controls, andin another embodiment, the selected genomic DNA is a test genomic DNA.Methylation levels can be determined by methods that are routine andknown to the skilled person. For example, methylation levels can becalculated as the fraction of ‘C’ bases at a target CpG site out of‘C’+‘U’ bases following the bisulfite treatment, or the fraction of ‘C’bases at a target CpG site out of total ‘C’+‘T’ bases following thebisulfite treatment and subsequent nucleic acid amplification, asdescribed herein.

A methylation score (MS) for a selected genomic DNA can be calculated bydetermining the normalized methylation difference (z-score) at aparticular site i (e.g., CpG site) with reference to a set of baselinesamples, converting the z-score for each site into a probability ofobserving such a z-score or greater (e.g., a one-sided p-value), andcombining the p-values into a final, aggregate methylation score.Optionally, the p-values are weighted. Each of these steps is detailedherein and immediately below.

Methylation scores are initially determined for a training set of normalgenomic DNA samples. First, a normalized methylation difference(z-score) at a particular site i (e.g., CpG site) is determinedaccording to the formula

$Z_{i} = \frac{\chi_{i} - \mu_{i}}{\sigma_{i}}$

wherein Z_(i) represents a normalized methylation difference for aparticular site identified as i, χ_(i) represents the methylation levelat site i in a member of the training set of normal genomic DNA, μ_(i)represents the mean methylation level at site i in the baseline samples,and σ_(i) represents the standard deviation of methylation levels atsite i in the baseline samples.

The z-score for each CpG site i (Z_(i)) is then converted into theprobability of observing such a z-score or greater. In one aspect, theprobability is calculated by converting the z-score into a one-sidedp-value (p_(i)). Probabilities can be calculated assuming a normaldistribution, t-distribution, or binomial distribution. Statisticaltools for such calculations are well known and easily available to aperson of ordinary skill.

Next, a methylation score (MS), an aggregate of the probability of theobserved normalized methylation differences, is determined by combiningthe p-values according to the Fisher formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}{\ln\left( p_{i} \right)}}}$

wherein p_(i) represents the one-sided p-value at site i, and krepresents the total number of sites. A methylation score is calculatedfor each member of the training set of normal genomic DNA.

Optionally, the p-value at each CpG site can be weighted by multiplyingthe p-value at each CpG site i (p_(i)) with a weighting factor w_(i),where w_(i) can correspond to the significance of the CpG site obtainedfrom a priori knowledge, the depth of coverage associated with the site,or any other ranking method. In this aspect, a methylation score(represented as MS) is determined by combining the weighted p-valuesaccording to the Fisher formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}{\ln\left( {w_{i}p_{i}} \right)}}}$

wherein p_(i) represents the one-sided p-value at site i, k representsthe total number of sites, and w_(i) represents the significance, forinstance coverage, of the site i. Use of this formula for determiningweighted methylation scores for a training set of normal genomic DNAsamples is illustrated in Example III.

Statistical analysis of the training set methylation scores is thenperformed. The mean methylation score (μ_(MS)) and standard deviation ofmethylation scores (σ_(MS)) in the training set of normal genomic DNAare calculated. This characterizes the distribution of the methylationscore in a normal population, and can be used to determine whether thegenomic DNA of a test genomic sample has an aberrant methylation level.

The methylation score (MS) of a test genomic DNA is then determined withreference to the baseline samples (as described above for members of thetraining set) and compared to the distribution of the methylation scoresdetermined for the training set of normal genomic DNA.

As described above in connection with the training set, a normalizedmethylation difference (z-score) at a particular site i (e.g., CpG site)is first determined according to the formula

$Z_{i} = \frac{\chi_{i} - \mu_{i}}{\sigma_{i}}$

wherein Z_(i) represents a normalized methylation difference for aparticular site identified as i, χ_(i) represents the methylation levelat site i in the test genomic DNA, μ_(i) represents the mean methylationlevel at site i in the baseline samples, and σ_(i) represents thestandard deviation of methylation levels at site i in the baselinesamples.

The z-score for each CpG site i (Z_(i)) is then converted into theprobability of observing such a z-score or greater. In one aspect, theprobability is calculated by converting the z-score into a one-sidedp-value (p_(i)). Probabilities can be calculated assuming a normaldistribution, t-distribution, or binomial distribution. A methylationscore (MS) of the test genomic DNA is determined by combining thep-values according to the Fisher formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}{\ln\left( p_{i} \right)}}}$

wherein p_(i) represents the one-sided p-value at site i, and krepresents the total number of sites.

Optionally, the p-value at each CpG site can be weighted by multiplyingthe p-value at each CpG site i (p_(i)) with a weight w₁, where w_(i) cancorrespond to the significance of the CpG site obtained from a prioriknowledge, the depth of coverage associated with the site, or any otherranking method. A methylation score (MS) of the test genomic DNA isdetermined by combining the weighted p-values according to the Fisherformula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}{\ln\left( {w_{i}p_{i}} \right)}}}$

wherein p_(i) represents the one-sided p-value at site i, k representsthe total number of sites, and w_(i) represents the significance, forinstance coverage, of the site i. Use of this formula for determiningweighted methylation scores for test genomic DNA samples is illustratedin Examples II and III.

Finally, the methylation score of the test genomic DNA is evaluatedagainst the distribution of methylation scores determined for thetraining set population, represented by the mean methylation score(μ_(MS)) and standard deviation of methylation scores (σ_(MS)) for thetraining set of normal genomic DNA. The number of standard deviationsthe methylation score for the test genomic DNA is from the methylationscore mean (μ_(MS)) of the training set of normal genomic DNA isdetermined according to the formula

$Z_{MS} = \frac{{MS} - \mu_{MS}}{\sigma_{MS}}$

wherein Z_(MS) represents a normalized methylation score difference, MSrepresents the methylation score of the test sample, μ_(MS) representsthe mean methylation score for the training set of normal genomic DNA,and σ_(MS) represents the standard deviation of methylation scores forthe training set of normal genomic DNA. Use of this formula fordetermining normalized methylation score difference is illustrated inExample III. A Z_(MS) value of greater than 1.5, greater than 2, greaterthan 2.5, or greater than 3 standard deviations indicates the testgenomic DNA has an aberrant DNA methylation level. In a preferredembodiment, a Z_(MS) value greater than 3 standard deviations is used asan indication that the test genomic DNA has an aberrant DNA methylationlevel.

In another embodiment, the methods set forth herein can be used toidentify a change in methylation state for a test organism or to monitorsuch changes over time. Accordingly, the present disclosure provides amethod that includes steps of (a) providing methylation states for aplurality of sites (e.g., CpG sites) in baseline genomic DNA from two ormore normal individual organisms; (b) determining, for each of the sites(e.g., CpG sites), the mean methylation level and standard deviation ofmethylation levels for the baseline genomic DNA; (c) providing a testdata set that includes (i) methylation states for the plurality of sites(e.g., CpG sites) from a first test genomic DNA from at least one testorganism, and optionally (ii) coverage at each of the sites (e.g., CpGsites) for detection of the methylation states; (d) determining, foreach of the sites (e.g., CpG sites), the methylation difference betweenthe first test genomic DNA and the baseline genomic DNA, therebyproviding a normalized methylation difference for the first test genomicDNA; (e) converting the normalized methylation difference for the firsttest genomic DNA at each of the sites (e.g., CpG sites) into theprobability of observing such a normalized methylation difference orgreater (e.g., a one-sided p-value), and optionally weighting theprobability of such an event; (f) determining a methylation score forthe first test genomic DNA; (g) repeating steps (c) through (f) using asecond test genomic DNA provided from a sample comprising a mixture ofgenomic DNA from a plurality of different cell types from the at leastone test organism, and using the same baseline genomic DNA; and (h)determining whether or not a change has occurred in the methylationscore between the first test genomic DNA and the second test genomicDNA.

An alternative method of monitoring changes in DNA methylation over timeincludes the steps of (a) providing methylation states for a pluralityof sites (e.g., CpG sites) in baseline genomic DNA from two or morenormal individual organisms; (b) determining, for each of the sites(e.g., CpG sites), the mean methylation level and standard deviation ofmethylation levels for the baseline genomic DNA; (c) providing a mixtureof genomic DNA from a test organism suspected of having a conditionassociated with an aberrant DNA methylation level (e.g., cancer),wherein the mixture comprises genomic DNA from a plurality of differentcell types from the test organism, thereby providing a first testgenomic DNA; (d) detecting methylation states for the plurality of sites(e.g., CpG sites) in the first test genomic DNA, and optionallydetermining the coverage at each of the sites (e.g., CpG sites) for thedetecting of the methylation states; (e) determining, for each of thesites (e.g., CpG sites), the methylation difference between the firsttest genomic DNA and the baseline genomic DNA, thereby providing anormalized methylation difference for the first test genomic DNA; (f)converting the normalized methylation difference for the first testgenomic DNA at each of the sites (e.g., CpG sites) into the probabilityof observing such a normalized methylation difference or greater (e.g.,a one-sided p-value), and optionally weighting the probability of suchan event; (g) determining a methylation score for the first test genomicDNA; (h) repeating steps (c) through (g) using a second test genomic DNAprovided from a sample comprising a mixture of genomic DNA from aplurality of different cell types from the at least one test organism,and using the same baseline genomic DNA; and (i) determining whether ornot a change has occurred in the methylation score between the firsttest genomic DNA and the second test genomic DNA.

First and second test genomic DNA samples (or test data sets) that arecompared in a method set forth herein can be derived from the same typeof cell, community, tissue or fluid, but at different time points.Accordingly, a method set forth herein can be used to identify ormonitor a change that occurs over time. In some embodiments thedifferent time points can occur before, during and/or after a particulartreatment. For example, in the case of monitoring or prognosing cancer,samples can be obtained from an individual before and after initiationof a treatment such as surgery, chemotherapy or radiation therapy.Furthermore multiple samples can be obtained at different time pointsduring treatment. For example the samples can be obtained and evaluatedat time points throughout surgery (e.g. to evaluate whether or notmargins have been cleared of cancerous tissue) or at different timepoints throughout a course of chemotherapy or radiation therapy.Different samples can be obtained from an individual and tested aftertreatment for example to test for relapse and remission.

In a further example, gut metagenomics samples can be obtained beforeand after a treatment (e.g. for a digestive disorder). The methylationstates of the samples can be evaluated and compared to identify changesin the bacterial flora of the gut due to the treatment. The changes inturn can be used to monitor the treatment and determine a prognosis forthe individual being treated.

Any of a variety of sample types set forth herein, or known in the artto contain tumor DNA, can be used in a method for identifying ormonitoring a change in methylation state for an individual. Observedchanges can provide a basis for diagnosis, prognosis, or screening of anindividual with respect to having a particular condition such as cancer.

A method set forth herein can also be used to screen or test a candidatetreatment, for example, in an experimental cell culture, tissue ororganism. Accordingly, a method set forth herein can be used to identifyor monitor a change that occurs over time in a cell culture, tissue ororganism being tested in a clinical or laboratory environment. In someembodiments the different time points can occur before, during and/orafter a particular candidate treatment. For example, samples can beobtained from a test organism before and after initiation of a candidatetreatment such as surgery, chemotherapy or radiation therapy.Furthermore, multiple samples can be obtained at different time pointsduring the candidate treatment. For example the samples can be obtainedand evaluated at time points throughout surgery (e.g. to evaluatewhether or not margins have been cleared of cancerous tissue) or atdifferent time points throughout a course of a candidate chemotherapy orradiation therapy. Different samples can be obtained from a testorganism and tested after a candidate treatment, for example, toevaluate relapse and remission. Control organisms that are not subjectedto the candidate treatment and/or that do not have a particularcondition can also be tested using similar methods. Comparison ofresults between samples subjected to candidate treatments and controlscan be used to determine efficacy and/or safety of a particularcandidate treatment

Any of a variety of sample types set forth herein, or known in the artto contain tumor DNA, can be used in a method for identifying orscreening a candidate treatment. Changes, whether or not being comparedto a particular control, can be used for evaluating efficacy and/orsafety of a particular candidate treatment.

In particular embodiments, this disclosure provides a method fordetecting a condition such as cancer. The method can include steps of(a) providing a mixture of genomic DNA from an individual suspected ofhaving the condition (e.g. cancer), wherein the mixture comprisesgenomic DNA from a plurality of different cell types from theindividual, thereby providing test genomic DNA; (b) detectingmethylation states for a plurality of sites (e.g. CpG sites) in the testgenomic DNA; (c) determining the coverage at each of the sites (e.g. CpGsites) for the detecting of the methylation states; (d) providingmethylation states for the plurality of sites (e.g. CpG sites) inreference genomic DNA from at least one reference individual, thereference individual being known to have the condition (e.g. cancer) orknown to not have the condition (e.g. cancer); (e) determining, for eachof the sites (e.g. CpG sites), the methylation difference between thetest genomic DNA and the reference genomic DNA, thereby providing anormalized methylation difference for each site (e.g. CpG site); (f)weighting the normalized methylation difference for each site (e.g. CpGsite) by the coverage at each of the sites (e.g. CpG sites), therebydetermining an aggregate coverage-weighted normalized methylationdifference score; and (g) determining that the individual does or doesnot have the condition (e.g. cancer) based on the aggregatecoverage-weighted normalized methylation difference score. In someembodiments the sample is blood and the DNA can, for example, includecell free DNA from the blood.

Also provided is a method for identifying a change in a condition suchas cancer. The method can include steps of (a) providing a mixture ofgenomic DNA from an individual suspected of having the condition (e.g.cancer), wherein the mixture comprises genomic DNA from a plurality ofdifferent cell types from the individual, thereby providing test genomicDNA; (b) detecting methylation states for a plurality of sites (e.g. CpGsites) in the test genomic DNA; (c) determining the coverage at each ofthe sites (e.g. CpG sites) for the detecting of the methylation states;(d) providing methylation states for the plurality of sites (e.g. CpGsites) in reference genomic DNA from at least one reference individual,the reference individual being known to have the condition (e.g. cancer)or known to not have the condition (e.g. cancer); (e) determining, foreach of the sites (e.g. CpG sites), the methylation difference betweenthe test genomic DNA and the reference genomic DNA, thereby providing anormalized methylation difference for each site (e.g. CpG site); (f)weighting the normalized methylation difference for each site (e.g. CpGsite) by the coverage at each of the sites (e.g. CpG sites), therebydetermining an aggregate coverage-weighted normalized methylationdifference score; and (g) repeating steps (a) through (f) using a secondmixture of genomic DNA from the individual suspected of having thecondition (e.g. cancer), and using the same reference genomic DNA fromthe at least one reference individual, and (h) determining whether ornot a change has occurred in the aggregate coverage-weighted normalizedmethylation difference score for the second test genomic DNA compared tothe test genomic DNA, thereby determining that a change has or has notoccurred in the condition (e.g. cancer) based on the change in theaggregate coverage-weighted normalized methylation difference score.

In particular embodiments, this disclosure provides a method fordetecting a condition such as cancer. The method can include steps of(a) providing methylation states for a plurality of sites (e.g., CpGsites) in baseline genomic DNA from at least one normal individualorganism; (b) determining, for each of the sites (e.g., CpG sites), themean methylation level and standard deviation of methylation levels forthe baseline genomic DNA; (c) providing a training set of normal genomicDNA samples from two or more normal individual organisms that includes(i) methylation states for a plurality of sites (e.g., CpG sites) in thetraining set of normal genomic DNA samples, and optionally (ii) coverageat each of the sites (e.g., CpG sites) for detection of the methylationstates; (d) determining, for each of the sites (e.g., CpG sites), themethylation difference between each normal genomic DNA sample of thetraining set and the baseline genomic DNA, thereby providing anormalized methylation difference for each normal genomic DNA sample ofthe training set at each site (e.g., CpG site); (e) converting thenormalized methylation difference for each normal genomic DNA sample ofthe training set at each site (e.g., CpG site) into the probability ofobserving such a normalized methylation difference or greater (e.g., aone-sided p-value), and optionally weighting the probability of such anevent; (f) determining a methylation score for each normal genomic DNAsample of the training set to obtain training set methylation scores;(g) calculating the mean methylation score and standard deviation of thetraining set methylation scores; (h) providing a mixture of genomic DNAfrom a test organism suspected of having the condition (e.g., cancer),wherein the mixture comprises genomic DNA from a plurality of differentcell types from the test organism, thereby providing test genomic DNA;(i) detecting methylation states for the plurality of sites (e.g., CpGsites) in the test genomic DNA, and optionally determining the coverageat each of the sites (e.g., CpG sites) for the detecting of themethylation states; (j) determining, for each of the sites (e.g., CpGsites), the methylation difference between the test genomic DNA and thebaseline genomic DNA, thereby providing a normalized methylationdifference for the test genomic DNA; (k) converting the normalizedmethylation difference for the test genomic DNA at each of the sites(e.g., CpG sites) into the probability of observing such a normalizedmethylation difference or greater (e.g., a one-sided p-value), andoptionally weighting the probability of such an event; (l) determining amethylation score for the test genomic DNA; and (m) comparing themethylation score of the test genomic DNA to the mean methylation scoreand standard deviation of methylation scores in the training set ofnormal genomic DNA to determine the number of standard deviations themethylation score of the test genomic DNA is from the distribution ofmethylation scores in the training set of normal genomic DNA. In theevent the number of standard deviations exceeds a predeterminedthreshold value (e.g., 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, etc.),the test sample is considered to have an aberrant DNA methylation level.

Optionally, the sites from the test genomic DNA are derived from aplurality of different cell types from the individual test organism, andas a further option, the cell type from which each of the sites (e.g.,CpG sites) is derived is unknown. In a further optional embodiment, theindividual test organism and the one or more baseline individualorganisms, training individual organisms, or a combination thereof arethe same species. In some embodiments, the mixture of genomic DNA froman individual suspected of having the condition is blood and the DNAcan, for example, include cell-free DNA (cfDNA) or circulating tumor DNA(ctDNA) from the blood.

Also provided herein is a method for identifying a change in a conditionsuch as cancer over time. The method can include steps of (a) providingmethylation states for a plurality of sites (e.g., CpG sites) inbaseline genomic DNA from at least one normal individual organism; (b)determining, for each of the sites (e.g., CpG sites), the meanmethylation level and standard deviation of methylation levels for thebaseline genomic DNA; (c) providing a first mixture of genomic DNA froma test organism suspected of having the condition (e.g., cancer),wherein the first mixture comprises genomic DNA from a plurality ofdifferent cell types from the test organism, thereby providing a firsttest genomic DNA; (d) detecting methylation states for the plurality ofsites (e.g., CpG sites) in the first test genomic DNA, and optionallydetermining the coverage at each of the sites (e.g., CpG sites) for thedetecting of the methylation states; (e) determining, for each of thesites (e.g., CpG sites), the methylation difference between the firsttest genomic DNA and the baseline genomic DNA, thereby providing anormalized methylation difference for the first test genomic DNA; (f)converting the normalized methylation difference for the first testgenomic DNA at each of the sites (e.g., CpG sites) into the probabilityof observing such a normalized methylation difference or greater (e.g.,a one-sided p-value), and optionally weighting the probability of suchan event; (g) determining a methylation score for the first test genomicDNA; (h) repeating steps (c) through (g) using a second mixture ofgenomic DNA from the test organism suspected of having the condition(e.g., cancer), wherein the second mixture comprises a second testgenomic DNA, and (i) determining whether or not a change has occurred inthe methylation score for the second test genomic DNA compared to thefirst test genomic DNA, thereby determining that a change has or has notoccurred in the condition (e.g., cancer) based on the change in themethylation score.

Methylation states determined using methods set forth herein can be usedfor molecular classification and prediction of cancers using criteriathat have been developed for gene expression and other genomic data(see, for example, Golub et al. (1999) Molecular classification ofcancer: class discovery and class prediction by gene expressionmonitoring. Science, 286, 531-537.). Other classification systems thatcan be used include those that have been developed for correlatingglobal changes in methylation pattern to molecular classification inbreast cancer (see, for example, Huang et al. (1999) Methylationprofiling of CpG sites in human breast cancer cells. Hum Mol Genet, 8,459-470), or those developed for correlating methylation patterns intumor suppressor genes (for example, p16, a cyclin-dependent kinaseinhibitor) in certain human cancer types (see, for example, Herman etal. (1995) Inactivation of the CDKN2/p16/MTS1 gene is frequentlyassociated with aberrant DNA methylation in all common human cancers.Cancer Res, 55, 4525-4530.; Otterson et al. (1995) CDKN2 gene silencingin lung cancer by DNA hypermethylation and kinetics of p16INK4 proteininduction by 5-aza 2′deoxycytidine. Oncogene, 11, 1211-1216.). The abovereferences are incorporated herein by reference.

In some applications of the methylation analysis methods set forthherein, a model can be developed to predict the disease type withoutprior pathological diagnosis. Thus, in some embodiments, the methods setforth herein are used to determine methylation patterns in staged tumorsamples relative to matched normal tissues from the same patient. Thedetermined differences in methylation pattern between the tumor andnormal tissues can be used to build a model to predict, diagnose ormonitor cancer. For example, methylation patterns determined for a testsample can be compared to a methylation pattern from a known normaland/or from a known tumor, and a diagnosis can be made based on thedegree of similarity of the test sample to one or both of thesereferences.

In addition, the methods set forth herein can facilitate identification,classification and prognostic evaluation of tumors. This information canin turn be used to identify subgroups of tumors with related properties.Such classification has been useful in identifying the causes of varioustypes of cancer and in predicting their clinical behavior.

In particular embodiments of the present methods, cancers are predicted,detected, identified, classified, or monitored from cell free DNA ofcancer patients. For example, the determination of a methylation patternfrom a plasma sample can be used to screen for cancer. When themethylation pattern of the plasma sample is aberrant compared with ahealthy reference, cancer may be suspected. Then further confirmationand assessment of the type of cancer or tissue origin of the cancer canbe performed by determining the plasma profile of methylation atdifferent genomic loci or by plasma genomic analysis to detecttumor-associated copy number aberrations, chromosomal translocations andsingle nucleotide variants. Alternatively, radiological and imaginginvestigations (e.g. computed tomography, magnetic resonance imaging,positron emission tomography) or endoscopy (e.g. upper gastrointestinalendoscopy or colonoscopy) can be used to further investigate individualswho were suspected of having cancer based on the plasma methylationlevel analysis.

In one aspect of the present invention, provided herein is a method forusing methylation levels to identify or classify a specific type ofcancer in a test organism, preferably a mammalian organism, morepreferably a human. In this aspect, methylation levels of a test genomicDNA are evaluated, for subsets of preselected methylation sitesassociated with known cancer types, herein referred to as“hypermethylated” sites, and then ranked from lowest to highest. Thecancer type corresponding to the highest average methylation level isconsidered to be associated with the test genomic DNA, i.e. the cancertype is deemed to be present in the test organism.

As a starting point, the method can include identifying specific cancersthat can be used as a cancer type in the identification orclassification algorithm according to this aspect of the invention. Acancer type is a cancer, e.g., breast invasive carcinoma, colonadenocarcinoma, lung adenocarcinoma, and others, that can be used as amember of a panel of specific cancers to determine whether a testorganism has a specific type of cancer.

Determining whether a cancer can be used as a cancer type in the presentmethod includes obtaining genomic DNA sequence data from clinicalsamples. Genomic DNA sequence data useful herein is readily availablefrom known databases that characterize genomic and epigenomicchanges—such as changes in methylation state—in different types ofcancers. The greater the number of clinical samples of a cancer in adatabase, the more likely the cancer can be used as a cancer type. Acancer type suitable for the present method may be defined using genomicDNA sequence data from at least 10, at least 15, at least 20, at least25, at least 30, at least 40, at least 50, at least 75, or at least 100clinical samples of a specific cancer.

Once a panel of suitable cancer type has been defined, a list ofso-called “hypermethylated” sites specific for each cancer type isassembled. In some embodiments, useful methylation sites that can beevaluated for methylation state include the selected CpG sites of thePan Cancer Panel set forth in Table I (the listed methylation sites arefrom Genome Build 37) and/or set forth in Table II (the listedmethylation sites are from Genome Build 37). In other embodiments,useful methylation sites that can be evaluated for methylation stateinclude those present in The Cancer Genome Atlas (see, for example,Cancer Genome Atlas Research Network et al., Nature Genetics45:1113-1120 (2013)), the CpG sites used to identify or monitorcolorectal cancer described in Worthley et al., Oncogene 29, 1653-1662(2010), and methylation markers for detection of ovarian cancer setforth in US Pat. App. Pub. No. 2008/0166728 A1, among others. All of thecited documents are incorporated herein by reference in theirentireties. All or a subset of the sites set forth herein or listed in areference herein can be used in the identification or classificationmethod set forth herein. For example, at least 100, 1×10³, 1×10⁴, 1×10⁵,1×10⁶, or more of the methylation sites can be used as a starting point.In some embodiments, the entire methylome (i.e. the full set ofmethylation sites in a test organism's genome) may be used to selecthypermethylated sites suitable for the present method.

TABLE I Pan Cancer Panel cg00006948 cg00012992 cg00019137 cg00021108cg00026375 cg00027037 cg00039627 cg00041084 cg00056676 cg00059034cg00073771 cg00073780 cg00079563 cg00081574 cg00091964 cg00107016cg00114393 cg00114963 cg00115040 cg00117463 cg00121634 cg00121640cg00124160 cg00128353 cg00132108 cg00134776 cg00136947 cg00139244cg00141174 cg00143220 cg00145489 cg00151810 cg00155423 cg00157987cg00158254 cg00164196 cg00168514 cg00169305 cg00183340 cg00196372cg00202702 cg00205263 cg00207389 cg00208931 cg00210994 cg00214530cg00220517 cg00221969 cg00233079 cg00235260 cg00235337 cg00245538cg00246817 cg00251610 cg00254802 cg00259618 cg00262031 cg00264591cg00266918 cg00267325 cg00275232 cg00280758 cg00281977 cg00283576cg00286984 cg00288050 cg00289081 cg00291351 cg00302521 cg00303548cg00303672 cg00310855 cg00311654 cg00312474 cg00318608 cg00322319cg00325599 cg00338893 cg00341980 cg00344260 cg00346326 cg00350003cg00351011 cg00352349 cg00353340 cg00358220 cg00365470 cg00367047cg00370303 cg00371920 cg00372486 cg00381697 cg00389976 cg00395632cg00397851 cg00401880 cg00404838 cg00405843 cg00407729 cg00408906cg00414171 cg00414398 cg00415978 cg00419564 cg00440043 cg00442814cg00447632 cg00449821 cg00450312 cg00456894 cg00466108 cg00466364cg00470794 cg00471966 cg00474209 cg00476317 cg00480136 cg00483446cg00485296 cg00485849 cg00486611 cg00486627 cg00487870 cg00488787cg00489861 cg00495503 cg00498155 cg00499289 cg00503704 cg00504703cg00507727 cg00512374 cg00525503 cg00527440 cg00533620 cg00549463cg00549910 cg00551736 cg00552973 cg00559018 cg00560547 cg00562243cg00567696 cg00574530 cg00576301 cg00577109 cg00581731 cg00582881cg00583303 cg00586537 cg00588720 cg00591844 cg00593962 cg00594560cg00594866 cg00598730 cg00603617 cg00607526 cg00611485 cg00613753cg00614641 cg00616965 cg00618725 cg00622677 cg00626110 cg00633736cg00639886 cg00642494 cg00643111 cg00650006 cg00651829 cg00656387cg00656411 cg00658626 cg00659495 cg00661753 cg00663739 cg00673557cg00679738 cg00683895 cg00687122 cg00691999 cg00692763 cg00695712cg00697033 cg00702008 cg00704633 cg00708380 cg00709515 cg00712044cg00713925 cg00719143 cg00720475 cg00735962 cg00741789 cg00744920cg00745606 cg00747619 cg00751156 cg00752016 cg00752376 cg00755836cg00757182 cg00758584 cg00761787 cg00769520 cg00769843 cg00773459cg00780574 cg00790649 cg00793280 cg00796360 cg00796793 cg00797346cg00800993 cg00803088 cg00803816 cg00808740 cg00809888 cg00813603cg00815093 cg00818693 cg00819163 cg00821073 cg00824141 cg00828602cg00834400 cg00835429 cg00838874 cg00843352 cg00845942 cg00846483cg00850971 cg00868383 cg00873850 cg00877887 cg00879003 cg00879447cg00880074 cg00884221 cg00887511 cg00894435 cg00896540 cg00899483cg00901765 cg00906644 cg00907211 cg00918541 cg00919118 cg00929855cg00939301 cg00940278 cg00944142 cg00948275 cg00953355 cg00953777cg00954566 cg00964103 cg00965391 cg00966974 cg00969047 cg00969787cg00971804 cg00976157 cg00977805 cg00979348 cg00983637 cg00984694cg00992385 cg00994693 cg00996262 cg00996986 cg00999950 cg01009697cg01012280 cg01015652 cg01016553 cg01016662 cg01019028 cg01024009cg01025398 cg01029840 cg01033463 cg01035198 cg01043524 cg01045612cg01052477 cg01054622 cg01060026 cg01060059 cg01069256 cg01070355cg01070794 cg01076997 cg01078147 cg01079098 cg01079658 cg01083689cg01093319 cg01097611 cg01097881 cg01097964 cg01098237 cg01099231cg01099875 cg01101742 cg01105385 cg01107801 cg01108392 cg01112082cg01112965 cg01126855 cg01141237 cg01142386 cg01143579 cg01143804cg01145317 cg01151699 cg01152936 cg01153132 cg01156948 cg01157070cg01160085 cg01175682 cg01179851 cg01181350 cg01183053 cg01184522cg01186777 cg01187533 cg01191114 cg01196517 cg01204271 cg01211349cg01215936 cg01216370 cg01224730 cg01226806 cg01227006 cg01228636cg01236148 cg01244034 cg01244650 cg01245656 cg01246835 cg01247426cg01250961 cg01254575 cg01256674 cg01258587 cg01259619 cg01263075cg01263292 cg01263716 cg01267522 cg01268683 cg01269344 cg01269537cg01270246 cg01274625 cg01275523 cg01277490 cg01280202 cg01284881cg01289643 cg01294263 cg01300341 cg01307939 cg01308268 cg01310019cg01310600 cg01313313 cg01313518 cg01315063 cg01316109 cg01327552cg01333350 cg01335685 cg01335781 cg01339444 cg01340952 cg01341170cg01363902 cg01367393 cg01369082 cg01370014 cg01370063 cg01370181cg01372071 cg01373292 cg01377006 cg01380710 cg01384163 cg01385795cg01387945 cg01394093 cg01398050 cg01402994 cg01404988 cg01406536cg01409659 cg01414358 cg01418667 cg01419567 cg01421405 cg01424281cg01429635 cg01431908 cg01434487 cg01442844 cg01445942 cg01446203cg01450204 cg01451328 cg01453694 cg01458686 cg01460805 cg01462053cg01464969 cg01466678 cg01479818 cg01483681 cg01485010 cg01486752cg01492246 cg01498231 cg01499217 cg01504555 cg01505590 cg01505767cg01506130 cg01506492 cg01507044 cg01507046 cg01511379 cg01514859cg01518631 cg01522456 cg01532206 cg01544751 cg01548300 cg01549404cg01555604 cg01556502 cg01558040 cg01562471 cg01563031 cg01564068cg01565690 cg01566304 cg01573616 cg01583034 cg01583969 cg01587630cg01588748 cg01593751 cg01597066 cg01601746 cg01606085 cg01606998cg01607295 cg01611886 cg01612478 cg01613306 cg01616178 cg01616474cg01618114 cg01618829 cg01632300 cg01637011 cg01637551 cg01641096cg01645113 cg01646610 cg01646639 cg01649597 cg01653005 cg01656221cg01657408 cg01665212 cg01666600 cg01667646 cg01667837 cg01677561cg01678172 cg01678714 cg01682021 cg01692340 cg01699584 cg01706029cg01706789 cg01719157 cg01719793 cg01722423 cg01722994 cg01725608cg01740172 cg01740424 cg01742897 cg01743617 cg01743962 cg01754037cg01754155 cg01757209 cg01763173 cg01764954 cg01772385 cg01775260cg01778450 cg01780109 cg01782227 cg01785417 cg01791407 cg01794473cg01814098 cg01814945 cg01822124 cg01831896 cg01833212 cg01834210cg01839688 cg01844352 cg01844539 cg01851208 cg01857475 cg01861574cg01862172 cg01869273 cg01869826 cg01871428 cg01873234 cg01876194cg01885963 cg01890984 cg01893041 cg01908537 cg01911237 cg01912921cg01922936 cg01940943 cg01947949 cg01948390 cg01949798 cg01950665cg01952683 cg01955962 cg01962509 cg01962510 cg01966612 cg01967288cg01969058 cg01970519 cg01970575 cg01970784 cg01978544 cg01981187cg01984858 cg01992107 cg01995821 cg01998047 cg02002584 cg02005505cg02012576 cg02012731 cg02014107 cg02020882 cg02028389 cg02030493cg02033141 cg02034497 cg02048890 cg02052895 cg02059348 cg02062409cg02065704 cg02072885 cg02078870 cg02079933 cg02086858 cg02096492cg02101625 cg02110141 cg02114924 cg02115050 cg02115539 cg02120463cg02120582 cg02126753 cg02128087 cg02132163 cg02132470 cg02134353cg02136132 cg02138756 cg02144298 cg02151754 cg02162906 cg02169113cg02169734 cg02172579 cg02174225 cg02180498 cg02196227 cg02200939cg02202600 cg02202980 cg02205739 cg02215070 cg02219071 cg02221750cg02221866 cg02229097 cg02229993 cg02231729 cg02232273 cg02233216cg02236651 cg02241397 cg02247561 cg02248320 cg02250071 cg02251557cg02257090 cg02257750 cg02260353 cg02265056 cg02266348 cg02270183cg02273903 cg02277383 cg02282626 cg02284150 cg02284587 cg02285922cg02286547 cg02290238 cg02293228 cg02298956 cg02303897 cg02304863cg02306127 cg02307033 cg02307605 cg02310733 cg02315971 cg02316216cg02328010 cg02330121 cg02330494 cg02331143 cg02340915 cg02344926cg02346970 cg02352687 cg02352723 cg02353937 cg02357043 cg02362467cg02362970 cg02369195 cg02384967 cg02388709 cg02394263 cg02398045cg02398612 cg02399645 cg02400449 cg02405503 cg02406285 cg02407785cg02408333 cg02409108 cg02424378 cg02425263 cg02430347 cg02435495cg02445664 cg02447380 cg02448922 cg02454595 cg02460997 cg02461665cg02470625 cg02472291 cg02474799 cg02481778 cg02483484 cg02485200cg02486351 cg02487654 cg02492778 cg02492791 cg02503395 cg02506053cg02510164 cg02511156 cg02513017 cg02513409 cg02527199 cg02532538cg02537163 cg02539714 cg02547025 cg02551396 cg02552311 cg02554246cg02557406 cg02557432 cg02558627 cg02560717 cg02565702 cg02567082cg02574526 cg02583334 cg02584489 cg02593205 cg02595750 cg02596331cg02602699 cg02604121 cg02608019 cg02617469 cg02617655 cg02618553cg02620228 cg02620694 cg02622885 cg02624855 cg02627531 cg02628801cg02630553 cg02633073 cg02636497 cg02637978 cg02638057 cg02639285cg02639993 cg02643218 cg02649987 cg02651961 cg02654360 cg02655739cg02657292 cg02659086 cg02659794 cg02660823 cg02664993 cg02665570cg02666434 cg02666504 cg02669964 cg02671646 cg02673256 cg02687055cg02688760 cg02690609 cg02692405 cg02701278 cg02708401 cg02711801cg02712555 cg02713068 cg02713266 cg02716516 cg02717437 cg02723311cg02725055 cg02737782 cg02738081 cg02739708 cg02750883 cg02757194cg02759151 cg02761345 cg02767177 cg02767539 cg02767960 cg02770946cg02776035 cg02776314 cg02783889 cg02784301 cg02787320 cg02795700cg02796773 cg02797548 cg02816363 cg02819231 cg02820717 cg02831090cg02835214 cg02836020 cg02836541 cg02838118 cg02841941 cg02842629cg02850812 cg02854695 cg02855409 cg02862354 cg02862904 cg02866454cg02867728 cg02871940 cg02873868 cg02874371 cg02876237 cg02877791cg02882044 cg02883595 cg02885694 cg02886549 cg02888838 cg02888906cg02891774 cg02892350 cg02892595 cg02892898 cg02893482 cg02899206cg02905065 cg02906238 cg02915746 cg02916964 cg02917917 cg02918146cg02918224 cg02921003 cg02921269 cg02921583 cg02927655 cg02929073cg02930242 cg02933119 cg02934500 cg02938682 cg02939019 cg02942594cg02945007 cg02951059 cg02951206 cg02951568 cg02952978 cg02954735cg02955219 cg02958718 cg02962318 cg02968116 cg02971481 cg02977761cg02978421 cg02980693 cg02982690 cg02982793 cg02983203 cg02993259cg02995055 cg03001116 cg03001832 cg03003689 cg03004714 cg03015433cg03016097 cg03016991 cg03024517 cg03024536 cg03031959 cg03038003cg03040279 cg03052869 cg03053575 cg03054643 cg03057213 cg03059112cg03060802 cg03065202 cg03071143 cg03081134 cg03082580 cg03088791cg03089869 cg03096401 cg03098937 cg03100040 cg03103035 cg03103770cg03108238 cg03113285 cg03116642 cg03122735 cg03125329 cg03132532cg03141007 cg03141069 cg03141620 cg03143697 cg03143742 cg03147990cg03148427 cg03153658 cg03157531 cg03159947 cg03165343 cg03168582cg03169767 cg03170611 cg03175305 cg03181829 cg03188118 cg03189990cg03191830 cg03192963 cg03202738 cg03209812 cg03210277 cg03217173cg03223126 cg03223733 cg03238298 cg03255556 cg03257575 cg03259494cg03265944 cg03273700 cg03279535 cg03288419 cg03290040 cg03297593cg03307911 cg03309367 cg03309726 cg03311339 cg03311459 cg03313212cg03315058 cg03321003 cg03324578 cg03328664 cg03332113 cg03334130cg03334540 cg03342084 cg03342530 cg03347559 cg03347944 cg03352181cg03356115 cg03356760 cg03364193 cg03365985 cg03366439 cg03366925cg03369344 cg03369477 cg03382304 cg03383295 cg03386480 cg03387066cg03391040 cg03392673 cg03393966 cg03397750 cg03405315 cg03410359cg03410436 cg03411979 cg03415695 cg03424727 cg03425504 cg03427543cg03430348 cg03430923 cg03431079 cg03434847 cg03437204 cg03443751cg03446195 cg03450370 cg03455458 cg03462055 cg03465206 cg03467027cg03468349 cg03470396 cg03472672 cg03476291 cg03479657 cg03485262cg03495059 cg03496713 cg03502284 cg03506609 cg03506979 cg03513246cg03521258 cg03524308 cg03525011 cg03526256 cg03532274 cg03535663cg03536983 cg03537779 cg03544918 cg03545133 cg03547745 cg03552151cg03552992 cg03554817 cg03556393 cg03556653 cg03559229 cg03562044cg03577052 cg03586803 cg03598499 cg03603214 cg03603951 cg03604840cg03607117 cg03607359 cg03608167 cg03609148 cg03609308 cg03611007cg03612722 cg03613077 cg03615913 cg03621100 cg03626278 cg03626734cg03631864 cg03638874 cg03648780 cg03650154 cg03662422 cg03663746cg03668475 cg03673687 cg03675739 cg03681341 cg03691812 cg03694261cg03695666 cg03699307 cg03701001 cg03701745 cg03704912 cg03707948cg03710481 cg03711182 cg03712038 cg03717315 cg03719634 cg03721976cg03722871 cg03735847 cg03738134 cg03741406 cg03751813 cg03753681cg03753849 cg03754311 cg03756448 cg03757145 cg03757871 cg03767822cg03768777 cg03770147 cg03771448 cg03774026 cg03776464 cg03778788cg03780545 cg03785281 cg03786924 cg03801902 cg03802907 cg03806238cg03808158 cg03809147 cg03817671 cg03817911 cg03818920 cg03818977cg03818992 cg03822259 cg03824617 cg03836414 cg03839661 cg03843031cg03846951 cg03860020 cg03860859 cg03861105 cg03863616 cg03871549cg03880509 cg03884587 cg03884792 cg03894174 cg03896436 cg03899372cg03900646 cg03901784 cg03909781 cg03911494 cg03920233 cg03921179cg03921416 cg03921599 cg03927893 cg03929741 cg03930088 cg03938598cg03940620 cg03947464 cg03954442 cg03961800 cg03974423 cg03978375cg03979582 cg03980991 cg03985136 cg03986989 cg03991848 cg03998871cg04005725 cg04008429 cg04010471 cg04011182 cg04012592 cg04012924cg04017769 cg04022379 cg04027074 cg04028634 cg04044297 cg04046599cg04049102 cg04051458 cg04054012 cg04057016 cg04058593 cg04072843cg04073970 cg04076682 cg04083712 cg04083751 cg04083753 cg04085025cg04089426 cg04092682 cg04095732 cg04099652 cg04106782 cg04110544cg04112845 cg04125371 cg04133572 cg04134305 cg04140862 cg04141379cg04145287 cg04148762 cg04156369 cg04159901 cg04167903 cg04171539cg04171853 cg04175417 cg04176674 cg04180299 cg04188397 cg04197823cg04199931 cg04199943 cg04206517 cg04209650 cg04216289 cg04219247cg04219613 cg04220088 cg04220579 cg04227789 cg04232325 cg04234680cg04235146 cg04243181 cg04245373 cg04258811 cg04261877 cg04262938cg04269188 cg04271801 cg04274487 cg04278225 cg04281219 cg04282206cg04283751 cg04285443 cg04292359 cg04297664 cg04307977 cg04309212cg04315947 cg04317977 cg04319464 cg04321580 cg04322105 cg04324727cg04342092 cg04343407 cg04352026 cg04352676 cg04356980 cg04360049cg04361852 cg04362858 cg04366249 cg04370807 cg04371288 cg04378874cg04380513 cg04385144 cg04385733 cg04389422 cg04389426 cg04391222cg04396685 cg04399418 cg04401038 cg04401710 cg04413680 cg04417028cg04424930 cg04430835 cg04435719 cg04437841 cg04438814 cg04439623cg04449512 cg04450862 cg04454506 cg04457626 cg04461388 cg04468564cg04480903 cg04487506 cg04489069 cg04493931 cg04494789 cg04501188cg04504095 cg04513669 cg04515583 cg04516083 cg04524120 cg04524652cg04525496 cg04534504 cg04539573 cg04539574 cg04543012 cg04547554cg04547588 cg04554033 cg04555982 cg04557953 cg04559178 cg04566848cg04568116 cg04569381 cg04572161 cg04577625 cg04578997 cg04580029cg04583043 cg04583285 cg04584833 cg04593780 cg04602387 cg04604884cg04605151 cg04614008 cg04614625 cg04614997 cg04618002 cg04618068cg04632671 cg04633513 cg04633600 cg04635736 cg04637598 cg04645567cg04653710 cg04657224 cg04657461 cg04658772 cg04660816 cg04661674cg04663870 cg04673837 cg04674803 cg04677163 cg04678565 cg04682916cg04697775 cg04722620 cg04727332 cg04730314 cg04736112 cg04744134cg04753439 cg04757428 cg04759335 cg04760021 cg04766136 cg04769392cg04772326 cg04775668 cg04777312 cg04782667 cg04787343 cg04796763cg04797985 cg04805619 cg04810377 cg04815758 cg04821107 cg04822330cg04822808 cg04824711 cg04828458 cg04830146 cg04832767 cg04836221cg04838747 cg04839422 cg04844977 cg04845053 cg04858616 cg04861263cg04863950 cg04865265 cg04870212 cg04873963 cg04877901 cg04880063cg04880618 cg04884481 cg04889106 cg04890495 cg04892170 cg04892391cg04897742 cg04912566 cg04920227 cg04922833 cg04923576 cg04932056cg04932544 cg04939555 cg04941721 cg04948038 cg04953079 cg04964944cg04965141 cg04968835 cg04970150 cg04972341 cg04974587 cg04975330cg04987465 cg04988287 cg04996355 cg04998420 cg05000136 cg05004321cg05004940 cg05011838 cg05014211 cg05021796 cg05022306 cg05026102cg05027458 cg05028467 cg05032399 cg05037168 cg05039463 cg05040544cg05040584 cg05041658 cg05044739 cg05052633 cg05052898 cg05056142cg05060672 cg05062612 cg05063339 cg05063412 cg05064297 cg05065669cg05071577 cg05071623 cg05072848 cg05075833 cg05084391 cg05086074cg05086811 cg05090851 cg05091519 cg05094548 cg05095123 cg05098471cg05098876 cg05099909 cg05102581 cg05103231 cg05103387 cg05109981cg05110787 cg05119363 cg05119480 cg05121790 cg05122861 cg05124235cg05125578 cg05127369 cg05127456 cg05129951 cg05133370 cg05139434cg05140069 cg05148722 cg05152561 cg05153364 cg05155840 cg05156550cg05167782 cg05173373 cg05173737 cg05194447 cg05195612 cg05196820cg05197625 cg05203877 cg05208423 cg05209770 cg05214218 cg05217962cg05224998 cg05228284 cg05228634 cg05235392 cg05237001 cg05249644cg05257472 cg05261559 cg05263743 cg05264446 cg05270922 cg05277991cg05284582 cg05287817 cg05288475 cg05294936 cg05295494 cg05296192cg05298677 cg05302784 cg05306225 cg05307923 cg05312305 cg05322217cg05323731 cg05329888 cg05331082 cg05332077 cg05332887 cg05336698cg05346841 cg05347473 cg05356308 cg05356662 cg05356738 cg05366156cg05367443 cg05371993 cg05372242 cg05376505 cg05376611 cg05377226cg05385047 cg05390530 cg05396178 cg05402599 cg05406943 cg05411953cg05412664 cg05419671 cg05426702 cg05428436 cg05428770 cg05432973cg05434115 cg05435286 cg05446010 cg05454237 cg05461906 cg05469285cg05470389 cg05481991 cg05482973 cg05488043 cg05495011 cg05502312cg05508067 cg05508761 cg05513983 cg05524529 cg05530751 cg05533953cg05542661 cg05542957 cg05547888 cg05554936 cg05557209 cg05565239cg05568274 cg05568797 cg05573182 cg05573997 cg05576451 cg05578357cg05578989 cg05580181 cg05585821 cg05598845 cg05603546 cg05605299cg05608159 cg05613002 cg05619888 cg05624214 cg05625889 cg05627083cg05628771 cg05629186 cg05633190 cg05638929 cg05645404 cg05657805cg05661333 cg05667158 cg05667817 cg05670408 cg05676541 cg05677402cg05681826 cg05682719 cg05683504 cg05684406 cg05688651 cg05692837cg05702737 cg05702851 cg05707458 cg05714219 cg05719720 cg05722552cg05724965 cg05725531 cg05730283 cg05739190 cg05740895 cg05747105cg05750029 cg05751100 cg05758094 cg05758434 cg05760393 cg05764240cg05768702 cg05770030 cg05775742 cg05775895 cg05777716 cg05787193cg05793299 cg05804949 cg05817664 cg05820312 cg05820448 cg05829482cg05831823 cg05833894 cg05837253 cg05842391 cg05843841 cg05851163cg05852040 cg05854826 cg05857941 cg05863587 cg05869503 cg05871997cg05875032 cg05882426 cg05891474 cg05893300 cg05899726 cg05906740cg05907237 cg05908775 cg05911003 cg05913514 cg05916744 cg05919561cg05920090 cg05924445 cg05928649 cg05930133 cg05935052 cg05937496cg05938671 cg05941624 cg05949800 cg05949903 cg05951864 cg05952925cg05953927 cg05956498 cg05962239 cg05963618 cg05970790 cg05975727cg05978988 cg05986781 cg05989429 cg05990544 cg05991857 cg05994148cg05995135 cg05995866 cg06001237 cg06008470 cg06011285 cg06011292cg06017917 cg06027057 cg06030619 cg06035247 cg06035702 cg06040683cg06041595 cg06045408 cg06046431 cg06053738 cg06055013 cg06055551cg06059849 cg06060853 cg06065141 cg06065225 cg06065743 cg06072021cg06073471 cg06078334 cg06080005 cg06082745 cg06100368 cg06102403cg06123396 cg06123544 cg06123783 cg06124975 cg06128195 cg06131143cg06161738 cg06163735 cg06172475 cg06177860 cg06188670 cg06193169cg06193383 cg06194536 cg06197981 cg06198190 cg06202686 cg06203207cg06205432 cg06214770 cg06217862 cg06225133 cg06226283 cg06226567cg06241792 cg06247015 cg06248179 cg06251832 cg06263193 cg06269673cg06274671 cg06283368 cg06285590 cg06285648 cg06287318 cg06289589cg06292947 cg06296570 cg06299537 cg06305340 cg06315607 cg06319033cg06320134 cg06321345 cg06322891 cg06323837 cg06326971 cg06327267cg06331595 cg06332859 cg06335741 cg06341054 cg06345952 cg06350353cg06353127 cg06353330 cg06368118 cg06369090 cg06369833 cg06371306cg06376016 cg06377152 cg06377278 cg06381123 cg06382894 cg06392318cg06395091 cg06396649 cg06403244 cg06406719 cg06407366 cg06407634cg06408864 cg06409432 cg06415582 cg06424594 cg06439293 cg06441668cg06443175 cg06447552 cg06448961 cg06457811 cg06458469 cg06458554cg06462964 cg06464594 cg06473927 cg06476192 cg06476344 cg06478457cg06479755 cg06480249 cg06481158 cg06482074 cg06487247 cg06488775cg06490744 cg06491924 cg06493473 cg06493664 cg06499484 cg06500654cg06503907 cg06508879 cg06512974 cg06523022 cg06525293 cg06531007cg06532037 cg06533200 cg06537829 cg06543087 cg06545361 cg06546406cg06554200 cg06562297 cg06562865 cg06563086 cg06564523 cg06567227cg06567525 cg06570167 cg06573644 cg06580371 cg06582752 cg06602857cg06604690 cg06606003 cg06609310 cg06615189 cg06616806 cg06620911cg06625414 cg06626087 cg06626126 cg06629767 cg06631603 cg06633429cg06636316 cg06636971 cg06637963 cg06650115 cg06654905 cg06657529cg06664930 cg06668844 cg06672317 cg06677538 cg06685177 cg06685724cg06689205 cg06690760 cg06692745 cg06694137 cg06699617 cg06700856cg06701027 cg06704170 cg06705435 cg06719671 cg06720017 cg06721528cg06722144 cg06722792 cg06727510 cg06728579 cg06734271 cg06739403cg06739520 cg06741043 cg06742044 cg06744574 cg06744978 cg06747745cg06749592 cg06755413 cg06759058 cg06763823 cg06765035 cg06768203cg06775072 cg06776588 cg06779469 cg06780705 cg06780839 cg06782692cg06785746 cg06787716 cg06787731 cg06787764 cg06791151 cg06792347cg06795971 cg06802147 cg06802365 cg06802658 cg06809055 cg06811478cg06820586 cg06822120 cg06829893 cg06830555 cg06833203 cg06837311cg06838175 cg06843320 cg06845943 cg06848047 cg06849515 cg06849719cg06851941 cg06861209 cg06862167 cg06862545 cg06864853 cg06867829cg06870284 cg06872519 cg06882758 cg06885524 cg06887260 cg06887275cg06888942 cg06890432 cg06892005 cg06893138 cg06893834 cg06897264cg06899530 cg06901369 cg06911121 cg06918887 cg06919916 cg06923622cg06928346 cg06931615 cg06936079 cg06936768 cg06937552 cg06945523cg06945936 cg06947852 cg06949053 cg06952671 cg06953252 cg06956232cg06958567 cg06959053 cg06959514 cg06960698 cg06963053 cg06967120cg06969287 cg06970228 cg06974483 cg06974515 cg06974871 cg06976202cg06978388 cg06982544 cg06983586 cg06985934 cg06996609 cg06997305cg07010314 cg07012770 cg07015190 cg07016493 cg07017175 cg07017901cg07025274 cg07025949 cg07026599 cg07031839 cg07045816 cg07046852cg07058507 cg07066369 cg07066755 cg07067241 cg07070305 cg07070934cg07072722 cg07074316 cg07077694 cg07079445 cg07085962 cg07089235cg07089892 cg07091779 cg07097925 cg07101782 cg07113947 cg07121078cg07121182 cg07122245 cg07126167 cg07127225 cg07127945 cg07130366cg07132633 cg07134033 cg07134254 cg07138603 cg07139265 cg07143052cg07147599 cg07148818 cg07148879 cg07151445 cg07152091 cg07152591cg07155336 cg07158434 cg07160574 cg07162257 cg07162665 cg07173547cg07175149 cg07175507 cg07180523 cg07184836 cg07190698 cg07191393cg07193041 cg07195282 cg07195941 cg07196014 cg07197823 cg07199619cg07201089 cg07203452 cg07207726 cg07208703 cg07209546 cg07211381cg07212035 cg07221635 cg07221967 cg07229938 cg07230107 cg07230380cg07231544 cg07233677 cg07235253 cg07235638 cg07251193 cg07256029cg07261734 cg07264124 cg07265700 cg07270078 cg07276078 cg07276861cg07279963 cg07290739 cg07291958 cg07293520 cg07297397 cg07297582cg07301433 cg07306531 cg07309124 cg07311521 cg07312552 cg07312854cg07313597 cg07313705 cg07314186 cg07315993 cg07316577 cg07319626cg07326648 cg07336230 cg07337234 cg07340574 cg07344025 cg07344492cg07346343 cg07365816 cg07366196 cg07371290 cg07376527 cg07377876cg07381778 cg07385577 cg07386190 cg07394446 cg07398614 cg07403338cg07408114 cg07414961 cg07420190 cg07421806 cg07423363 cg07438288cg07438617 cg07448606 cg07449645 cg07451080 cg07452097 cg07454491cg07457568 cg07459525 cg07464092 cg07467520 cg07469961 cg07470489cg07482795 cg07484673 cg07484808 cg07486895 cg07488684 cg07491796cg07498606 cg07513622 cg07519536 cg07528097 cg07536920 cg07537876cg07540542 cg07544244 cg07544748 cg07545317 cg07548580 cg07559696cg07559730 cg07560517 cg07562483 cg07563569 cg07564088 cg07567630cg07568344 cg07572131 cg07580038 cg07588964 cg07594209 cg07596524cg07602492 cg07614306 cg07618175 cg07620039 cg07620889 cg07621104cg07622404 cg07625131 cg07626874 cg07629187 cg07637123 cg07639650cg07642566 cg07647353 cg07648581 cg07649491 cg07650252 cg07657131cg07658614 cg07668802 cg07669489 cg07671603 cg07671858 cg07673866cg07681728 cg07682547 cg07682663 cg07687766 cg07689503 cg07693037cg07696033 cg07697981 cg07698901 cg07700369 cg07706362 cg07706463cg07714565 cg07715387 cg07718308 cg07721822 cg07725355 cg07733620cg07735777 cg07749724 cg07755173 cg07755653 cg07756196 cg07758574cg07759377 cg07760910 cg07776163 cg07781332 cg07799366 cg07802571cg07805238 cg07808348 cg07808546 cg07808661 cg07811212 cg07813370cg07813608 cg07817400 cg07817783 cg07824265 cg07824914 cg07825094cg07826275 cg07835844 cg07836661 cg07838681 cg07844931 cg07847233cg07847424 cg07847733 cg07850464 cg07864323 cg07869548 cg07870757cg07872652 cg07876898 cg07877431 cg07879727 cg07881061 cg07890490cg07896312 cg07900766 cg07900968 cg07904448 cg07914772 cg07914959cg07916884 cg07917127 cg07917886 cg07919108 cg07925282 cg07926482cg07927379 cg07929090 cg07940440 cg07944798 cg07944863 cg07945002cg07951978 cg07959338 cg07963121 cg07964538 cg07966910 cg07973470cg07976644 cg07980216 cg07980469 cg07980518 cg07984256 cg07985503cg08009993 cg08012199 cg08015704 cg08023179 cg08024409 cg08026502cg08027745 cg08028651 cg08033284 cg08038311 cg08042316 cg08044516cg08045063 cg08047802 cg08048987 cg08063340 cg08069899 cg08074201cg08074971 cg08079580 cg08083016 cg08089301 cg08089542 cg08102141cg08102256 cg08114257 cg08114373 cg08114476 cg08115387 cg08117032cg08120331 cg08131100 cg08132931 cg08134829 cg08135278 cg08135850cg08136813 cg08140343 cg08140387 cg08145698 cg08153160 cg08157579cg08157672 cg08158570 cg08164617 cg08169659 cg08170140 cg08170869cg08174718 cg08193467 cg08193910 cg08194677 cg08195448 cg08196106cg08198711 cg08204843 cg08205230 cg08210629 cg08210727 cg08213098cg08214689 cg08221811 cg08224646 cg08228715 cg08229488 cg08232264cg08244959 cg08252855 cg08253188 cg08255782 cg08266417 cg08269389cg08269900 cg08270258 cg08273666 cg08275602 cg08276739 cg08276889cg08278648 cg08279184 cg08282385 cg08289140 cg08303146 cg08309706cg08322205 cg08324950 cg08327269 cg08330183 cg08331513 cg08334310cg08335854 cg08341316 cg08343755 cg08343881 cg08344081 cg08345372cg08350814 cg08352292 cg08355316 cg08369872 cg08372619 cg08378505cg08378782 cg08382226 cg08382542 cg08382705 cg08384171 cg08385249cg08385874 cg08397273 cg08405284 cg08406370 cg08407486 cg08410921cg08416650 cg08418841 cg08419026 cg08424063 cg08424219 cg08428129cg08430489 cg08441931 cg08445409 cg08446255 cg08447479 cg08448833cg08460435 cg08463543 cg08466351 cg08468370 cg08473553 cg08478074cg08478189 cg08482682 cg08487399 cg08496276 cg08504049 cg08505228cg08506260 cg08511440 cg08514735 cg08517062 cg08517659 cg08518631cg08519216 cg08522707 cg08535260 cg08541345 cg08548095 cg08548429cg08552819 cg08553816 cg08555325 cg08565139 cg08572016 cg08576643cg08577384 cg08591091 cg08592707 cg08599259 cg08603188 cg08604885cg08607907 cg08617528 cg08620474 cg08623947 cg08625094 cg08625382cg08625556 cg08635685 cg08642016 cg08644023 cg08644993 cg08651867cg08653184 cg08658318 cg08658407 cg08659357 cg08669018 cg08671133cg08671343 cg08675193 cg08683343 cg08687901 cg08694014 cg08696866cg08697092 cg08703613 cg08707078 cg08708599 cg08712068 cg08714121cg08718398 cg08725962 cg08727443 cg08728856 cg08728892 cg08729135cg08731623 cg08736446 cg08740729 cg08745498 cg08747591 cg08748615cg08757742 cg08767710 cg08769300 cg08771731 cg08774231 cg08794135cg08794939 cg08797606 cg08805144 cg08806611 cg08809418 cg08810584cg08815286 cg08816037 cg08827358 cg08832069 cg08834269 cg08837308cg08839858 cg08845439 cg08846783 cg08851719 cg08853659 cg08855568cg08855729 cg08856879 cg08857159 cg08859513 cg08867893 cg08869573cg08875180 cg08881159 cg08886727 cg08888625 cg08890360 cg08892705cg08894362 cg08894629 cg08900833 cg08903381 cg08905325 cg08912051cg08914844 cg08915922 cg08920748 cg08925398 cg08933227 cg08934846cg08941714 cg08945452 cg08949339 cg08952590 cg08958015 cg08962682cg08965464 cg08984023 cg08990497 cg08992818 cg08993236 cg08995368cg08998501 cg09000112 cg09003539 cg09010998 cg09017894 cg09019052cg09024340 cg09033756 cg09041207 cg09043127 cg09044785 cg09052453cg09067818 cg09068665 cg09079593 cg09085842 cg09093388 cg09103591cg09106556 cg09130556 cg09133028 cg09135695 cg09140932 cg09150117cg09150450 cg09150633 cg09153683 cg09158745 cg09159405 cg09160477cg09163958 cg09168548 cg09169215 cg09170903 cg09177131 cg09183941cg09185650 cg09185773 cg09185829 cg09191750 cg09196068 cg09198448cg09202227 cg09210514 cg09219451 cg09224753 cg09229231 cg09231514cg09235885 cg09239756 cg09243759 cg09248482 cg09249112 cg09260640cg09265000 cg09267113 cg09267217 cg09267324 cg09267776 cg09268672cg09273054 cg09275910 cg09279615 cg09280946 cg09282289 cg09290735cg09290941 cg09306584 cg09307284 cg09312564 cg09313801 cg09315391cg09315918 cg09316894 cg09319202 cg09320746 cg09324698 cg09331704cg09345512 cg09350548 cg09353063 cg09359907 cg09366357 cg09369381cg09371047 cg09371439 cg09371456 cg09377064 cg09381701 cg09393675cg09400281 cg09405169 cg09405612 cg09411252 cg09413557 cg09417889cg09421347 cg09425356 cg09426197 cg09428893 cg09432613 cg09435617cg09449030 cg09450020 cg09451960 cg09455182 cg09458673 cg09463047cg09463882 cg09464194 cg09464488 cg09468912 cg09470640 cg09472203cg09472360 cg09479015 cg09482777 cg09486260 cg09508531 cg09516362cg09527109 cg09528265 cg09528884 cg09537107 cg09538135 cg09540952cg09542745 cg09545293 cg09546168 cg09546921 cg09547767 cg09552692cg09553358 cg09559047 cg09559792 cg09561458 cg09563244 cg09564020cg09564101 cg09567473 cg09580567 cg09586080 cg09592603 cg09593184cg09601704 cg09601770 cg09603188 cg09605726 cg09608387 cg09608712cg09611472 cg09615101 cg09628601 cg09631475 cg09640960 cg09642640cg09648315 cg09650487 cg09654046 cg09666654 cg09667582 cg09668408cg09669356 cg09670616 cg09674251 cg09679923 cg09681814 cg09690326cg09691340 cg09693388 cg09695735 cg09697651 cg09700210 cg09700630cg09701392 cg09710740 cg09717333 cg09717545 cg09722742 cg09723781cg09727692 cg09728607 cg09741070 cg09742170 cg09742442 cg09745440cg09749751 cg09750382 cg09753043 cg09758742 cg09764408 cg09765994cg09775582 cg09785391 cg09793279 cg09793584 cg09802608 cg09804380cg09809276 cg09810750 cg09813219 cg09819791 cg09822284 cg09823095cg09824782 cg09826587 cg09827065 cg09848789 cg09852007 cg09852693cg09854653 cg09857830 cg09858237 cg09859179 cg09859456 cg09865339cg09865760 cg09867230 cg09877593 cg09885086 cg09885499 cg09889291cg09892203 cg09906488 cg09906751 cg09908474 cg09909671 cg09913544cg09916572 cg09927508 cg09933058 cg09936190 cg09936645 cg09942248cg09947186 cg09949949 cg09952946 cg09956859 cg09967877 cg09968391cg09972364 cg09974136 cg09979256 cg09981184 cg09988062 cg09996156cg09997760 cg09998133 cg09998229 cg10004574 cg10025396 cg10025443cg10026473 cg10028348 cg10036727 cg10042106 cg10042319 cg10062141cg10069638 cg10072850 cg10092374 cg10095938 cg10096161 cg10096177cg10104986 cg10108389 cg10111450 cg10113334 cg10115191 cg10118828cg10124651 cg10126409 cg10128164 cg10129816 cg10135588 cg10140114cg10141019 cg10148270 cg10151741 cg10156125 cg10157203 cg10158080cg10159349 cg10163776 cg10165331 cg10166205 cg10167296 cg10169177cg10171063 cg10173182 cg10176059 cg10176160 cg10176687 cg10177238cg10185885 cg10188732 cg10192047 cg10194844 cg10197432 cg10201807cg10209083 cg10212705 cg10216353 cg10217327 cg10218490 cg10242089cg10243398 cg10247585 cg10259735 cg10266394 cg10272779 cg10273340cg10291288 cg10291555 cg10293093 cg10300229 cg10312211 cg10316381cg10316989 cg10317807 cg10328157 cg10330024 cg10333802 cg10336707cg10342908 cg10348922 cg10350352 cg10365447 cg10366407 cg10370599cg10377903 cg10381455 cg10392164 cg10398590 cg10408778 cg10418567cg10421688 cg10434152 cg10437806 cg10443195 cg10445911 cg10449070cg10453390 cg10457056 cg10461004 cg10463708 cg10473508 cg10474350cg10477603 cg10480343 cg10496915 cg10497692 cg10507402 cg10515332cg10515753 cg10516832 cg10517014 cg10517814 cg10518543 cg10523966cg10534923 cg10536962 cg10538151 cg10542883 cg10544031 cg10544564cg10547729 cg10548038 cg10548492 cg10548807 cg10555583 cg10557743cg10569039 cg10569615 cg10579840 cg10580473 cg10580941 cg10581281cg10588034 cg10588150 cg10589249 cg10590767 cg10592245 cg10592690cg10598066 cg10598353 cg10599985 cg10600178 cg10603183 cg10604396cg10606059 cg10608256 cg10611186 cg10611863 cg10612237 cg10615091cg10620273 cg10626559 cg10630028 cg10630240 cg10633931 cg10639440cg10639888 cg10641986 cg10648670 cg10649525 cg10657367 cg10661724cg10667877 cg10668041 cg10668399 cg10672508 cg10673839 cg10675666cg10677909 cg10678018 cg10685144 cg10685945 cg10688328 cg10689512cg10691592 cg10692118 cg10694030 cg10696724 cg10699264 cg10700334cg10701051 cg10701282 cg10705236 cg10706069 cg10706100 cg10706454cg10706642 cg10711209 cg10713073 cg10713773 cg10714492 cg10715930cg10718940 cg10721116 cg10722846 cg10725121 cg10725547 cg10726226cg10728469 cg10728756 cg10729325 cg10733249 cg10734734 cg10737195cg10739095 cg10748160 cg10749413 cg10754670 cg10758227 cg10764541cg10766373 cg10768690 cg10773309 cg10775273 cg10778599 cg10778619cg10779656 cg10790778 cg10798048 cg10799186 cg10802132 cg10804678cg10809491 cg10810183 cg10811474 cg10815543 cg10817615 cg10821050cg10821115 cg10822149 cg10827893 cg10832495 cg10832730 cg10832949cg10833091 cg10836173 cg10836716 cg10836887 cg10838664 cg10841775cg10847032 cg10851281 cg10851835 cg10852609 cg10853637 cg10858746cg10861017 cg10864074 cg10864596 cg10864855 cg10871779 cg10880755cg10883284 cg10884052 cg10884788 cg10885961 cg10886442 cg10890233cg10895922 cg10897376 cg10899637 cg10900437 cg10900932 cg10901968cg10908369 cg10911660 cg10911913 cg10914137 cg10918328 cg10923475cg10925433 cg10926113 cg10938374 cg10943191 cg10943348 cg10949611cg10950272 cg10951120 cg10954363 cg10955973 cg10971790 cg10974632cg10975000 cg10977320 cg10983327 cg10986864 cg10989300 cg10989504cg10994148 cg10995640 cg11005250 cg11007380 cg11007931 cg11012953cg11013544 cg11015038 cg11018723 cg11019211 cg11025763 cg11027354cg11029301 cg11031064 cg11032070 cg11038073 cg11038280 cg11038774cg11042361 cg11044163 cg11046315 cg11052668 cg11054379 cg11055358cg11056409 cg11059483 cg11062418 cg11062677 cg11065506 cg11067756cg11071762 cg11071960 cg11073571 cg11075316 cg11075416 cg11076617cg11081186 cg11081729 cg11086760 cg11090139 cg11092616 cg11094122cg11098493 cg11099006 cg11100602 cg11100798 cg11100836 cg11103652cg11107669 cg11108676 cg11109374 cg11116776 cg11117108 cg11117633cg11118396 cg11122493 cg11124426 cg11125787 cg11127711 cg11134155cg11140785 cg11142406 cg11142826 cg11144056 cg11150664 cg11152298cg11152364 cg11153872 cg11154608 cg11155707 cg11159091 cg11161828cg11163901 cg11165313 cg11169461 cg11170727 cg11171224 cg11173422cg11176990 cg11186405 cg11189251 cg11197015 cg11198358 cg11200965cg11201307 cg11206041 cg11206067 cg11208039 cg11218625 cg11223573cg11224647 cg11237751 cg11240320 cg11241206 cg11242978 cg11244813cg11245443 cg11246071 cg11248182 cg11248715 cg11249313 cg11251113cg11252337 cg11257429 cg11258043 cg11258164 cg11259628 cg11262757cg11262906 cg11263235 cg11263393 cg11267065 cg11267829 cg11270393cg11274962 cg11278262 cg11278506 cg11279425 cg11279933 cg11281912cg11283700 cg11285507 cg11286023 cg11287660 cg11292593 cg11294084cg11296230 cg11297723 cg11302533 cg11303127 cg11305077 cg11306587cg11313386 cg11315754 cg11316146 cg11316784 cg11320449 cg11326968cg11334214 cg11342277 cg11343017 cg11345323 cg11348338 cg11352190cg11352418 cg11352866 cg11353329 cg11353380 cg11358257 cg11360366cg11360768 cg11371122 cg11373604 cg11374425 cg11376305 cg11377484cg11378840 cg11381826 cg11383347 cg11386792 cg11387705 cg11388325cg11390468 cg11390957 cg11392855 cg11395536 cg11396791 cg11398523cg11399097 cg11400401 cg11401682 cg11401866 cg11402700 cg11407741cg11412935 cg11413715 cg11416076 cg11417653 cg11419456 cg11421604cg11422045 cg11423323 cg11424198 cg11425254 cg11425280 cg11428482cg11430245 cg11431820 cg11434468 cg11435087 cg11439004 cg11442717cg11444379 cg11444428 cg11444607 cg11445323 cg11459006 cg11459714cg11460029 cg11461030 cg11474250 cg11474317 cg11476211 cg11485463cg11486350 cg11487705 cg11490941 cg11494123 cg11495854 cg11503720cg11504515 cg11504897 cg11510060 cg11510557 cg11512800 cg11518359cg11518945 cg11521404 cg11522018 cg11522812 cg11523538 cg11525479cg11525941 cg11527373 cg11528832 cg11536457 cg11538128 cg11539424cg11540007 cg11542224 cg11542789 cg11546106 cg11550126 cg11551257cg11551740 cg11553248 cg11561737 cg11564981 cg11565355 cg11566061cg11567172 cg11569407 cg11569979 cg11571001 cg11571263 cg11576123cg11583963 cg11587925 cg11590405 cg11591325 cg11591516 cg11593482cg11595155 cg11596863 cg11600807 cg11607341 cg11610754 cg11612291cg11613229 cg11621911 cg11623861 cg11628574 cg11628754 cg11631644cg11638200 cg11643991 cg11644057 cg11647421 cg11654179 cg11654553cg11654620 cg11655629 cg11657970 cg11661868 cg11662264 cg11664467cg11665588 cg11666087 cg11667451 cg11671925 cg11672054 cg11676221cg11679177 cg11684496 cg11686528 cg11687406 cg11688696 cg11691561cg11692477 cg11694752 cg11695653 cg11696361 cg11696475 cg11697226cg11698944 cg11706467 cg11708616 cg11715903 cg11716665 cg11720950cg11721249 cg11723397 cg11727653 cg11729413 cg11729970 cg11732642cg11734710 cg11737232 cg11737710 cg11737742 cg11738446 cg11739541cg11739675 cg11740878 cg11747771 cg11750133 cg11750900 cg11751927cg11752440 cg11753239 cg11753867 cg11757144 cg11758861 cg11760700cg11765438 cg11767181 cg11768886 cg11771234 cg11772643 cg11775417cg11775528 cg11775605 cg11775837 cg11777390 cg11782779 cg11784071cg11785166 cg11787288 cg11790681 cg11792653 cg11794649 cg11794877cg11797092 cg11799819 cg11802864 cg11804724 cg11804775 cg11805669cg11806442 cg11811216 cg11812069 cg11820517 cg11828108 cg11829072cg11831043 cg11837274 cg11838688 cg11854154 cg11856078 cg11866527cg11879480 cg11879536 cg11882252 cg11884230 cg11889265 cg11889692cg11889769 cg11895474 cg11896633 cg11901043 cg11901188 cg11904590cg11906038 cg11911648 cg11912765 cg11916219 cg11921270 cg11922371cg11923957 cg11926764 cg11928730 cg11930114 cg11931727 cg11935854cg11944428 cg11946336 cg11948055 cg11948456 cg11949831 cg11951910cg11953272 cg11954479 cg11955541 cg11958643 cg11960115 cg11963595cg11963676 cg11967894 cg11968091 cg11972401 cg11973682 cg11980147cg11980481 cg11985220 cg11992351 cg11992375 cg11993230 cg11996434cg12005824 cg12013041 cg12014547 cg12015854 cg12019773 cg12020533cg12020549 cg12024304 cg12024311 cg12025027 cg12027636 cg12033943cg12034791 cg12038935 cg12040278 cg12040486 cg12041340 cg12042448cg12042503 cg12042659 cg12043314 cg12046254 cg12048031 cg12048674cg12055259 cg12056977 cg12057741 cg12059226 cg12060306 cg12062488cg12064069 cg12066398 cg12066864 cg12070152 cg12071806 cg12074093cg12074780 cg12077344 cg12080391 cg12082598 cg12082871 cg12083965cg12084869 cg12087304 cg12092561 cg12097080 cg12101108 cg12104945cg12108974 cg12109566 cg12110677 cg12119111 cg12121066 cg12128270cg12132563 cg12136088 cg12145080 cg12150111 cg12152867 cg12153044cg12164472 cg12164777 cg12169977 cg12177207 cg12178578 cg12180984cg12181634 cg12182940 cg12182991 cg12184864 cg12185399 cg12187115cg12187657 cg12189880 cg12205244 cg12205729 cg12207120 cg12210040cg12212657 cg12217936 cg12227762 cg12228245 cg12228659 cg12233208cg12236045 cg12240767 cg12243375 cg12244581 cg12249227 cg12253819cg12253830 cg12254291 cg12256538 cg12259256 cg12266841 cg12266953cg12271199 cg12273811 cg12275348 cg12276768 cg12280407 cg12281798cg12283120 cg12286569 cg12287025 cg12292531 cg12297030 cg12297619cg12298212 cg12298905 cg12300292 cg12304599 cg12311057 cg12315302cg12316475 cg12316865 cg12318400 cg12319507 cg12320306 cg12328890cg12330330 cg12332415 cg12334247 cg12339905 cg12340947 cg12342334cg12346504 cg12346874 cg12351660 cg12358524 cg12359315 cg12363807cg12367520 cg12368524 cg12371386 cg12372692 cg12373208 cg12377578cg12379940 cg12379948 cg12380339 cg12381164 cg12382902 cg12387700cg12391667 cg12392528 cg12393697 cg12394377 cg12396999 cg12397426cg12400781 cg12405785 cg12411093 cg12414653 cg12420858 cg12435415cg12439074 cg12441358 cg12443753 cg12448161 cg12449162 cg12449366cg12452364 cg12456286 cg12457529 cg12466737 cg12469475 cg12473009cg12476541 cg12477533 cg12480416 cg12482809 cg12484037 cg12484686cg12485161 cg12487147 cg12489736 cg12492094 cg12498522 cg12500891cg12500976 cg12501868 cg12503643 cg12505146 cg12508331 cg12511113cg12513284 cg12514620 cg12514873 cg12518360 cg12520861 cg12523878cg12527384 cg12536526 cg12537168 cg12537925 cg12538248 cg12541049cg12545252 cg12545721 cg12547474 cg12550412 cg12551582 cg12555306cg12558012 cg12558519 cg12559860 cg12561338 cg12563240 cg12563839cg12566138 cg12568633 cg12573516 cg12581244 cg12582734 cg12585429cg12586262 cg12586496 cg12587386 cg12588082 cg12589298 cg12592716cg12593303 cg12593608 cg12594716 cg12595016 cg12598803 cg12600289cg12610867 cg12612104 cg12613402 cg12619536 cg12620443 cg12622139cg12623648 cg12624667 cg12629875 cg12630336 cg12632097 cg12632573cg12639324 cg12643049 cg12643366 cg12643689 cg12647165 cg12652174cg12653917 cg12664560 cg12665504 cg12671336 cg12680730 cg12682392cg12685200 cg12685731 cg12686016 cg12691382 cg12693702 cg12695989cg12706983 cg12714014 cg12714180 cg12727462 cg12728517 cg12734954cg12735820 cg12741345 cg12745764 cg12745769 cg12748332 cg12758636cg12759298 cg12760327 cg12764921 cg12767281 cg12771118 cg12773604cg12775884 cg12777448 cg12778476 cg12784241 cg12784598 cg12784848cg12789062 cg12790391 cg12790592 cg12791192 cg12792011 cg12804278cg12810233 cg12810313 cg12814529 cg12815987 cg12819417 cg12825804cg12835256 cg12837084 cg12837552 cg12837896 cg12841273 cg12845520cg12850036 cg12850252 cg12851504 cg12853563 cg12853981 cg12859164cg12868019 cg12872647 cg12874092 cg12879909 cg12883479 cg12886634cg12886942 cg12891498 cg12895747 cg12902426 cg12908522 cg12909741cg12910175 cg12914047 cg12919976 cg12920814 cg12921914 cg12924825cg12926104 cg12929567 cg12934934 cg12936423 cg12936797 cg12938565cg12940104 cg12942328 cg12943155 cg12945693 cg12949975 cg12953628cg12954230 cg12962355 cg12966801 cg12968192 cg12970542 cg12970695cg12970724 cg12976501 cg12978205 cg12978575 cg12981137 cg12985773cg12986338 cg12989733 cg12992385 cg12998491 cg13000134 cg13002939cg13002945 cg13008717 cg13008978 cg13011362 cg13016179 cg13021384cg13021409 cg13023133 cg13024590 cg13027682 cg13031251 cg13031432cg13042543 cg13047843 cg13047892 cg13050058 cg13050981 cg13055665cg13056495 cg13065262 cg13067194 cg13070193 cg13070215 cg13071476cg13072057 cg13075263 cg13079047 cg13080151 cg13083436 cg13084429cg13085414 cg13094036 cg13100764 cg13107975 cg13108181 cg13109300cg13109397 cg13111733 cg13112361 cg13113115 cg13115661 cg13117031cg13119884 cg13120771 cg13121120 cg13127309 cg13131185 cg13132965cg13133420 cg13135031 cg13137376 cg13137533 cg13140564 cg13149662cg13158344 cg13161961 cg13165109 cg13165472 cg13168407 cg13168683cg13173260 cg13177786 cg13179469 cg13180809 cg13181079 cg13181745cg13185030 cg13186286 cg13192788 cg13194994 cg13195923 cg13197406cg13204512 cg13207797 cg13210260 cg13210403 cg13215078 cg13217116cg13223402 cg13227105 cg13227697 cg13231951 cg13233166 cg13235817cg13236378 cg13247581 cg13247671 cg13251269 cg13253980 cg13254898cg13255096 cg13258039 cg13258989 cg13259296 cg13262310 cg13264473cg13264741 cg13266630 cg13268132 cg13285189 cg13285637 cg13286510cg13298359 cg13299025 cg13307451 cg13308080 cg13309421 cg13319975cg13320114 cg13322582 cg13323825 cg13326227 cg13330524 cg13332538cg13334883 cg13336642 cg13337238 cg13337697 cg13338827 cg13340335cg13342441 cg13348059 cg13348155 cg13351117 cg13351830 cg13355047cg13359699 cg13360562 cg13361393 cg13362540 cg13362637 cg13365761cg13368085 cg13369164 cg13369291 cg13376290 cg13378886 cg13380624cg13383019 cg13384453 cg13386176 cg13386351 cg13388111 cg13390743cg13393601 cg13395035 cg13395410 cg13400018 cg13401831 cg13404421cg13405293 cg13412066 cg13414916 cg13415207 cg13417268 cg13419713cg13419791 cg13420848 cg13421439 cg13424209 cg13425637 cg13426138cg13427834 cg13432744 cg13436055 cg13436799 cg13437337 cg13437361cg13438334 cg13439181 cg13440083 cg13444964 cg13445199 cg13447915cg13447927 cg13448814 cg13449295 cg13451025 cg13451222 cg13455700cg13458335 cg13462028 cg13462843 cg13463054 cg13463683 cg13464924cg13465955 cg13467254 cg13468764 cg13470341 cg13472882 cg13475388cg13483916 cg13484295 cg13484581 cg13487102 cg13488395 cg13491481cg13491690 cg13492103 cg13495850 cg13499966 cg13501181 cg13501359cg13501793 cg13504245 cg13504570 cg13507506 cg13510327 cg13512069cg13514129 cg13516820 cg13517567 cg13518792 cg13521097 cg13521908cg13522244 cg13523528 cg13525837 cg13529912 cg13530158 cg13530621cg13535593 cg13536757 cg13536876 cg13537781 cg13538845 cg13541961cg13542964 cg13548601 cg13550107 cg13552710 cg13554489 cg13554744cg13554903 cg13556659 cg13561476 cg13562911 cg13563903 cg13565301cg13568334 cg13568946 cg13570982 cg13574390 cg13577493 cg13578447cg13579428 cg13580857 cg13582072 cg13588054 cg13589330 cg13590336cg13593479 cg13595904 cg13601427 cg13604337 cg13604887 cg13605988cg13607835 cg13609667 cg13615338 cg13617204 cg13617889 cg13618596cg13618906 cg13620096 cg13620744 cg13629999 cg13632230 cg13632876cg13634713 cg13636404 cg13636408 cg13636880 cg13640626 cg13643376cg13643814 cg13644629 cg13647349 cg13648312 cg13649886 cg13650104cg13650149 cg13652445 cg13652513 cg13655570 cg13661131 cg13661211cg13662093 cg13665266 cg13670601 cg13670911 cg13683125 cg13683218cg13686589 cg13690354 cg13690989 cg13694746 cg13695877 cg13699804cg13700897 cg13702942 cg13703584 cg13708218 cg13708497 cg13710037cg13710703 cg13714039 cg13714067 cg13716829 cg13719188 cg13724379cg13730743 cg13736068 cg13740698 cg13744663 cg13746315 cg13747794cg13749494 cg13750123 cg13750967 cg13758646 cg13758712 cg13761375cg13762060 cg13764828 cg13766030 cg13768269 cg13769223 cg13773705cg13773914 cg13776340 cg13782728 cg13784621 cg13785718 cg13786801cg13787982 cg13788258 cg13788592 cg13789548 cg13789775 cg13792037cg13792045 cg13795264 cg13795465 cg13797228 cg13802457 cg13803214cg13807970 cg13808240 cg13816282 cg13818875 cg13821072 cg13822158cg13822726 cg13823553 cg13826167 cg13828183 cg13829980 cg13830081cg13831922 cg13836270 cg13837913 cg13845147 cg13846866 cg13847066cg13848566 cg13849454 cg13852218 cg13852879 cg13854983 cg13856711cg13861122 cg13861524 cg13868165 cg13872965 cg13875120 cg13876163cg13876462 cg13879442 cg13885320 cg13885965 cg13887004 cg13894748cg13899314 cg13901737 cg13903378 cg13905264 cg13911393 cg13911723cg13912673 cg13915212 cg13916322 cg13917712 cg13921956 cg13924755cg13930105 cg13930300 cg13931559 cg13932362 cg13932603 cg13936125cg13938881 cg13945578 cg13945644 cg13946902 cg13947666 cg13947892cg13950625 cg13951042 cg13951527 cg13951572 cg13952031 cg13952656cg13952745 cg13955436 cg13955512 cg13957721 cg13965724 cg13969239cg13971154 cg13975009 cg13975625 cg13979305 cg13980528 cg13980570cg13984623 cg13987972 cg13993336 cg13993853 cg13995101 cg13995769cg13997864 cg14008500 cg14012112 cg14015503 cg14018731 cg14021564cg14022137 cg14026459 cg14028892 cg14035550 cg14036923 cg14038484cg14041778 cg14044057 cg14045486 cg14046986 cg14048874 cg14049461cg14051592 cg14052221 cg14055374 cg14057961 cg14059420 cg14066751cg14067419 cg14074052 cg14081631 cg14084176 cg14084312 cg14087806cg14089512 cg14089984 cg14090923 cg14093886 cg14096353 cg14097019cg14097171 cg14098468 cg14099335 cg14101302 cg14107807 cg14114517cg14114673 cg14117643 cg14119437 cg14122696 cg14123923 cg14128634cg14135025 cg14136927 cg14140647 cg14140881 cg14141331 cg14142965cg14145593 cg14146583 cg14149680 cg14150327 cg14154651 cg14156760cg14157855 cg14157947 cg14159790 cg14161579 cg14162034 cg14162120cg14163229 cg14163665 cg14163802 cg14172603 cg14174099 cg14174215cg14176132 cg14180718 cg14181169 cg14183693 cg14183976 cg14184277cg14190674 cg14190761 cg14191292 cg14192160 cg14192858 cg14193886cg14194048 cg14195122 cg14199276 cg14200979 cg14202478 cg14202910cg14203032 cg14204255 cg14207210 cg14207954 cg14209005 cg14209793cg14210694 cg14212266 cg14212504 cg14214865 cg14215776 cg14216029cg14218042 cg14218513 cg14219599 cg14220081 cg14220654 cg14221039cg14222879 cg14223395 cg14224937 cg14225021 cg14231871 cg14232289cg14233355 cg14233568 cg14235416 cg14237975 cg14239329 cg14241138cg14241323 cg14242042 cg14242958 cg14244963 cg14246859 cg14247332cg14250130 cg14251622 cg14254380 cg14257369 cg14261472 cg14262439cg14263942 cg14265936 cg14266251 cg14267263 cg14270789 cg14272275cg14275457 cg14276323 cg14277392 cg14278575 cg14282137 cg14283380cg14283569 cg14283602 cg14286439 cg14287868 cg14288330 cg14291066cg14291622 cg14293362 cg14294629 cg14294793 cg14298966 cg14304761cg14307443 cg14309384 cg14311471 cg14311608 cg14312959 cg14314818cg14316898 cg14318946 cg14320054 cg14320593 cg14322429 cg14326413cg14326460 cg14326885 cg14327951 cg14329441 cg14334286 cg14335782cg14336879 cg14339043 cg14339599 cg14340610 cg14344315 cg14345980cg14347989 cg14348439 cg14349843 cg14351528 cg14354472 cg14355192cg14360865 cg14364356 cg14365570 cg14368881 cg14369405 cg14370784cg14372333 cg14372394 cg14373456 cg14378848 cg14381390 cg14381972cg14385175 cg14385337 cg14393469 cg14395787 cg14398275 cg14399078cg14399851 cg14400631 cg14402472 cg14404812 cg14405753 cg14406256cg14406628 cg14409559 cg14412416 cg14415214 cg14415616 cg14415885cg14417372 cg14419581 cg14421860 cg14424049 cg14425722 cg14425863cg14426525 cg14426781 cg14430974 cg14431699 cg14443380 cg14444140cg14447369 cg14449803 cg14453704 cg14455998 cg14457918 cg14458834cg14458955 cg14460343 cg14461650 cg14464464 cg14464509 cg14465900cg14469376 cg14471619 cg14472025 cg14473522 cg14475915 cg14477452cg14477745 cg14489346 cg14489594 cg14494448 cg14494623 cg14494933cg14497202 cg14497673 cg14497851 cg14497940 cg14500250 cg14503180cg14510404 cg14512363 cg14514226 cg14517108 cg14519123 cg14519153cg14519294 cg14519850 cg14522427 cg14523810 cg14526204 cg14528318cg14530233 cg14532412 cg14536899 cg14537243 cg14539978 cg14541950cg14543508 cg14543941 cg14545970 cg14549262 cg14552376 cg14552401cg14553600 cg14554167 cg14557534 cg14558428 cg14562083 cg14562158cg14563868 cg14567287 cg14567414 cg14568217 cg14569471 cg14570307cg14575484 cg14578525 cg14579819 cg14580982 cg14582226 cg14582929cg14583869 cg14584031 cg14584529 cg14587446 cg14588896 cg14589148cg14590817 cg14592406 cg14592830 cg14595275 cg14595911 cg14597534cg14598143 cg14598950 cg14601136 cg14601938 cg14602341 cg14602640cg14603040 cg14603466 cg14610853 cg14611892 cg14612785 cg14615868cg14620039 cg14620549 cg14621784 cg14625631 cg14626375 cg14627760cg14629287 cg14629509 cg14630839 cg14632002 cg14633426 cg14633783cg14634210 cg14637067 cg14638988 cg14642298 cg14650464 cg14652031cg14652629 cg14653643 cg14655649 cg14661159 cg14664764 cg14665414cg14665716 cg14665813 cg14669514 cg14670303 cg14670974 cg14671526cg14675211 cg14677681 cg14678099 cg14683738 cg14685095 cg14686012cg14689953 cg14696535 cg14697835 cg14707092 cg14707269 cg14708847cg14711866 cg14713933 cg14719076 cg14723344 cg14725151 cg14730085cg14738643 cg14739039 cg14741685 cg14743462 cg14743593 cg14750844cg14751544 cg14759342 cg14772118 cg14774364 cg14776114 cg14777772cg14780004 cg14784699 cg14786398 cg14791866 cg14793086 cg14795119cg14806773 cg14808132 cg14808329 cg14808890 cg14809226 cg14811608cg14814333 cg14816482 cg14823287 cg14826226 cg14831838 cg14831990cg14834850 cg14841337 cg14847688 cg14850412 cg14851108 cg14855657cg14856893 cg14859047 cg14859854 cg14860120 cg14865717 cg14867604cg14868703 cg14869141 cg14870242 cg14876685 cg14883448 cg14883916cg14895945 cg14897419 cg14898260 cg14898822 cg14900295 cg14900984cg14901232 cg14902356 cg14905632 cg14906390 cg14910061 cg14910368cg14914519 cg14914982 cg14916315 cg14921743 cg14922886 cg14929100cg14929757 cg14930059 cg14931304 cg14932794 cg14933159 cg14937343cg14940405 cg14943877 cg14944944 cg14946389 cg14948290 cg14950855cg14953397 cg14955300 cg14960592 cg14962363 cg14970096 cg14971597cg14975184 cg14978307 cg14980060 cg14982133 cg14984246 cg14985481cg14986699 cg14986962 cg14987048 cg14988503 cg14989642 cg14990333cg14992155 cg14992232 cg14993820 cg14995036 cg14997226 cg14998713cg15007391 cg15012766 cg15014826 cg15019790 cg15020425 cg15024936cg15034135 cg15038664 cg15042080 cg15042995 cg15044073 cg15048660cg15051226 cg15052854 cg15057150 cg15057442 cg15061981 cg15063322cg15063355 cg15068050 cg15069758 cg15071611 cg15071854 cg15075170cg15077376 cg15078013 cg15088000 cg15088777 cg15089487 cg15089892cg15090202 cg15091323 cg15097584 cg15097762 cg15099632 cg15100227cg15104702 cg15111475 cg15112395 cg15112775 cg15118872 cg15119076cg15119377 cg15123035 cg15123881 cg15124968 cg15127661 cg15128200cg15129823 cg15131414 cg15131977 cg15133344 cg15134628 cg15137760cg15138883 cg15139482 cg15139588 cg15139745 cg15140191 cg15144236cg15145693 cg15147435 cg15147690 cg15151685 cg15154191 cg15156614cg15158376 cg15160780 cg15160843 cg15164958 cg15167155 cg15167646cg15170424 cg15170743 cg15173150 cg15174311 cg15174834 cg15174951cg15177071 cg15179113 cg15179725 cg15179955 cg15185001 cg15185479cg15190738 cg15193782 cg15195321 cg15201536 cg15208756 cg15210108cg15211499 cg15211655 cg15212349 cg15218775 cg15219650 cg15226147cg15232894 cg15233183 cg15240568 cg15245095 cg15247645 cg15248577cg15249639 cg15257259 cg15257376 cg15258033 cg15258847 cg15264083cg15264811 cg15267010 cg15269503 cg15270697 cg15278646 cg15279950cg15282018 cg15283062 cg15289427 cg15292101 cg15301489 cg15306209cg15308062 cg15308664 cg15310637 cg15311382 cg15312298 cg15313226cg15316583 cg15318396 cg15318690 cg15319517 cg15320948 cg15321298cg15322430 cg15322570 cg15323252 cg15324424 cg15325978 cg15326320cg15326795 cg15331902 cg15336997 cg15340644 cg15353061 cg15353444cg15355118 cg15355420 cg15356923 cg15358633 cg15359501 cg15361065cg15364537 cg15365320 cg15367082 cg15369512 cg15369743 cg15377586cg15382497 cg15382696 cg15384856 cg15385562 cg15386368 cg15386964cg15387132 cg15389490 cg15390554 cg15391499 cg15392269 cg15392489cg15394350 cg15396395 cg15397448 cg15401067 cg15402399 cg15407528cg15410253 cg15410276 cg15412736 cg15414833 cg15415545 cg15420387cg15420692 cg15421606 cg15424054 cg15424250 cg15424739 cg15430464cg15433056 cg15434569 cg15438497 cg15442262 cg15442811 cg15442988cg15444978 cg15446670 cg15447479 cg15448907 cg15450734 cg15454726cg15454895 cg15461105 cg15461888 cg15462174 cg15462457 cg15463628cg15471079 cg15471812 cg15472210 cg15472403 cg15473325 cg15473751cg15475080 cg15475576 cg15476279 cg15479068 cg15480095 cg15481636cg15482792 cg15485323 cg15486374 cg15488251 cg15490840 cg15490944cg15491911 cg15493051 cg15494405 cg15496005 cg15496063 cg15498349cg15504677 cg15528091 cg15533075 cg15540596 cg15542798 cg15542994cg15543919 cg15545624 cg15546187 cg15546607 cg15547669 cg15554007cg15554438 cg15558982 cg15562399 cg15562912 cg15564428 cg15571277cg15571330 cg15572012 cg15573925 cg15575982 cg15578015 cg15583014cg15584445 cg15586779 cg15591513 cg15592187 cg15592945 cg15595044cg15597257 cg15598442 cg15599946 cg15601228 cg15603957 cg15611279cg15612063 cg15612845 cg15615350 cg15616946 cg15617019 cg15617847cg15620146 cg15620384 cg15621338 cg15622672 cg15623249 cg15623480cg15623503 cg15623573 cg15627078 cg15628498 cg15631323 cg15632325cg15637095 cg15642792 cg15643885 cg15645220 cg15645634 cg15646782cg15648026 cg15651650 cg15653282 cg15658487 cg15664899 cg15672146cg15674129 cg15679144 cg15684724 cg15684917 cg15685268 cg15685943cg15689967 cg15692239 cg15692535 cg15698568 cg15699267 cg15700487cg15704369 cg15705469 cg15710245 cg15713801 cg15717066 cg15717808cg15718162 cg15722404 cg15724773 cg15726314 cg15732502 cg15734706cg15736338 cg15736978 cg15737365 cg15739717 cg15745385 cg15745619cg15748470 cg15748490 cg15757271 cg15758138 cg15759056 cg15759616cg15770728 cg15773137 cg15774465 cg15777964 cg15778012 cg15786541cg15787284 cg15792252 cg15799279 cg15804105 cg15804767 cg15808558cg15809694 cg15811719 cg15819171 cg15820273 cg15822346 cg15824316cg15825116 cg15825186 cg15830431 cg15837382 cg15840419 cg15842116cg15843401 cg15846316 cg15846643 cg15847198 cg15847614 cg15852572cg15860695 cg15863148 cg15863924 cg15864491 cg15869383 cg15871647cg15873303 cg15875437 cg15879316 cg15879949 cg15885628 cg15889017cg15889804 cg15890274 cg15891422 cg15892839 cg15893070 cg15896339cg15897970 cg15900320 cg15900657 cg15904764 cg15906409 cg15907310cg15909933 cg15910486 cg15914011 cg15914589 cg15915129 cg15916166cg15916192 cg15917116 cg15919045 cg15919396 cg15920867 cg15921240cg15924577 cg15924985 cg15925383 cg15928538 cg15929693 cg15931721cg15933823 cg15934415 cg15934994 cg15939253 cg15940724 cg15942481cg15946212 cg15946251 cg15946259 cg15947940 cg15955291 cg15955731cg15958576 cg15963563 cg15965542 cg15966876 cg15968604 cg15969216cg15971888 cg15974272 cg15976650 cg15977719 cg15978014 cg15981995cg15987088 cg15991262 cg15991471 cg15996043 cg15996499 cg15999899cg16005828 cg16005847 cg16006242 cg16007619 cg16008609 cg16008616cg16011583 cg16011800 cg16012041 cg16012111 cg16014770 cg16016176cg16019856 cg16019898 cg16021775 cg16024834 cg16026627 cg16030427cg16034881 cg16040495 cg16040504 cg16048372 cg16048568 cg16049364cg16049707 cg16056105 cg16056849 cg16057262 cg16060486 cg16061012cg16063519 cg16064478 cg16068063 cg16070123 cg16079396 cg16079958cg16080450 cg16082336 cg16083800 cg16088123 cg16093752 cg16094298cg16095615 cg16095951 cg16096796 cg16098064 cg16101148 cg16110940cg16112844 cg16113298 cg16114640 cg16117248 cg16117910 cg16126280cg16127719 cg16128701 cg16129172 cg16132520 cg16134800 cg16136098cg16139934 cg16140432 cg16141316 cg16144193 cg16146177 cg16149238cg16150677 cg16154689 cg16159491 cg16162324 cg16162668 cg16169604cg16172313 cg16172408 cg16172657 cg16173736 cg16174274 cg16176048cg16176568 cg16178625 cg16179047 cg16180217 cg16181043 cg16185831cg16187092 cg16189024 cg16189346 cg16190350 cg16195569 cg16196984cg16200496 cg16203801 cg16208507 cg16216704 cg16219491 cg16219810cg16226745 cg16229376 cg16230141 cg16232979 cg16234029 cg16235861cg16236766 cg16238795 cg16246366 cg16246961 cg16248783 cg16250330cg16250461 cg16253259 cg16254764 cg16257681 cg16263224 cg16266453cg16267579 cg16268214 cg16268778 cg16268848 cg16271017 cg16274199cg16275739 cg16276070 cg16276153 cg16277128 cg16285203 cg16285380cg16288248 cg16291962 cg16292168 cg16293631 cg16296290 cg16296826cg16297938 cg16300317 cg16306190 cg16307409 cg16313278 cg16315928cg16317459 cg16318768 cg16319691 cg16321523 cg16328251 cg16329784cg16330517 cg16331920 cg16334629 cg16335926 cg16339096 cg16347317cg16351364 cg16354201 cg16354296 cg16358446 cg16359169 cg16365799cg16366473 cg16368442 cg16370586 cg16372625 cg16373769 cg16374471cg16375948 cg16379337 cg16382256 cg16384885 cg16391783 cg16395486cg16403669 cg16404460 cg16405637 cg16406225 cg16406833 cg16409838cg16409840 cg16410706 cg16416045 cg16417447 cg16418684 cg16419066cg16423505 cg16424178 cg16424326 cg16426482 cg16430510 cg16430572cg16430909 cg16433211 cg16434547 cg16438397 cg16440629 cg16443424cg16443970 cg16446408 cg16447652 cg16452086 cg16458671 cg16460359cg16466899 cg16469099 cg16470309 cg16471850 cg16473511 cg16475705cg16475755 cg16478774 cg16480132 cg16487794 cg16489895 cg16492707cg16494477 cg16496687 cg16496895 cg16497277 cg16503618 cg16504335cg16506703 cg16508199 cg16512570 cg16515523 cg16519173 cg16521245cg16522462 cg16523137 cg16524830 cg16531271 cg16532600 cg16532755cg16533373 cg16533495 cg16534233 cg16534499 cg16535332 cg16537756cg16538178 cg16541852 cg16544887 cg16545496 cg16546016 cg16547110cg16549389 cg16551483 cg16557944 cg16558908 cg16564710 cg16564802cg16564842 cg16568036 cg16570019 cg16573136 cg16573178 cg16573328cg16573542 cg16576620 cg16577002 cg16579555 cg16580934 cg16583330cg16584595 cg16585911 cg16586442 cg16588061 cg16589663 cg16590005cg16595261 cg16596102 cg16600119 cg16601893 cg16602799 cg16611581cg16612379 cg16612742 cg16619071 cg16622511 cg16624692 cg16624787cg16627646 cg16628372 cg16630791 cg16632280 cg16633750 cg16633901cg16638266 cg16641818 cg16644177 cg16645133 cg16652651 cg16652790cg16654143 cg16654152 cg16654732 cg16655084 cg16655338 cg16655805cg16655905 cg16658099 cg16658130 cg16658719 cg16661554 cg16662989cg16663344 cg16663570 cg16663900 cg16672925 cg16673106 cg16675644cg16675700 cg16675708 cg16678602 cg16682989 cg16689800 cg16691033cg16692923 cg16692998 cg16693872 cg16696021 cg16696536 cg16696645cg16697731 cg16698835 cg16699174 cg16699861 cg16703576 cg16703914cg16704678 cg16704739 cg16704802 cg16705351 cg16705383 cg16705665cg16707062 cg16712637 cg16713262 cg16715162 cg16717099 cg16718445cg16718942 cg16718986 cg16728223 cg16729160 cg16732469 cg16733654cg16733855 cg16740476 cg16740905 cg16741308 cg16743811 cg16749570cg16750914 cg16751623 cg16751732 cg16753409 cg16754678 cg16758662cg16761549 cg16762077 cg16763192 cg16764580 cg16765006 cg16768441cg16772035 cg16776231 cg16777057 cg16780890 cg16782935 cg16785690cg16790239 cg16796590 cg16797300 cg16797714 cg16800028 cg16800708cg16800851 cg16801093 cg16801720 cg16802027 cg16802151 cg16803141cg16805065 cg16805150 cg16813053 cg16814399 cg16817229 cg16818909cg16818993 cg16821694 cg16822189 cg16822216 cg16822474 cg16832551cg16836995 cg16838838 cg16844403 cg16844989 cg16848054 cg16848116cg16848624 cg16853034 cg16858615 cg16859924 cg16862295 cg16862624cg16863718 cg16863990 cg16868994 cg16871994 cg16874579 cg16876647cg16879222 cg16882226 cg16883282 cg16884042 cg16884569 cg16885107cg16885296 cg16887890 cg16891895 cg16895792 cg16897885 cg16899280cg16899486 cg16903605 cg16906712 cg16909109 cg16914151 cg16914989cg16919771 cg16921310 cg16922058 cg16925307 cg16927253 cg16934685cg16936581 cg16938614 cg16944092 cg16953473 cg16958716 cg16962970cg16963683 cg16966815 cg16967640 cg16968865 cg16970851 cg16974909cg16977570 cg16978797 cg16983817 cg16984332 cg16991768 cg16994506cg16995983 cg16998537 cg17003293 cg17009433 cg17014953 cg17015511cg17016932 cg17018527 cg17022437 cg17023776 cg17025142 cg17026879cg17027195 cg17031478 cg17034030 cg17035091 cg17036833 cg17042926cg17043284 cg17045804 cg17046890 cg17049889 cg17052813 cg17052926cg17057514 cg17059853 cg17061505 cg17068985 cg17070611 cg17078980cg17081644 cg17081778 cg17088631 cg17091793 cg17092624 cg17093212cg17093995 cg17094378 cg17097950 cg17100218 cg17100322 cg17105013cg17106175 cg17106222 cg17107459 cg17108748 cg17114257 cg17125477cg17127587 cg17128349 cg17128996 cg17130063 cg17131030 cg17136126cg17142743 cg17143192 cg17145587 cg17146291 cg17146640 cg17147995cg17150306 cg17152789 cg17152869 cg17154605 cg17155612 cg17159473cg17161743 cg17164747 cg17167076 cg17169289 cg17169566 cg17170568cg17171962 cg17174375 cg17177755 cg17178280 cg17182507 cg17183414cg17185401 cg17186727 cg17188147 cg17188901 cg17191919 cg17192115cg17194182 cg17198587 cg17200447 cg17204213 cg17204394 cg17205316cg17210604 cg17215449 cg17217189 cg17218628 cg17222234 cg17225222cg17234305 cg17241016 cg17243637 cg17245188 cg17246382 cg17250302cg17251034 cg17252605 cg17255947 cg17259656 cg17268276 cg17269277cg17270520 cg17271180 cg17277729 cg17277949 cg17278573 cg17279338cg17283601 cg17284384 cg17285325 cg17287725 cg17287767 cg17290076cg17291435 cg17291521 cg17293641 cg17296166 cg17298489 cg17299935cg17304222 cg17304496 cg17306638 cg17318632 cg17318990 cg17321214cg17323027 cg17323409 cg17325959 cg17327184 cg17327401 cg17328790cg17329249 cg17332338 cg17334978 cg17345188 cg17345480 cg17346177cg17352157 cg17354190 cg17354785 cg17355083 cg17355294 cg17356181cg17360650 cg17372745 cg17376957 cg17379799 cg17383940 cg17384056cg17384898 cg17387989 cg17398233 cg17399352 cg17404915 cg17406148cg17411913 cg17412258 cg17412602 cg17416280 cg17417193 cg17418463cg17419241 cg17420983 cg17428043 cg17428324 cg17431401 cg17432022cg17432453 cg17432620 cg17434043 cg17435266 cg17438432 cg17439093cg17441778 cg17442482 cg17445007 cg17445044 cg17445120 cg17456266cg17459431 cg17460095 cg17465133 cg17468312 cg17468987 cg17470837cg17473382 cg17482197 cg17483297 cg17485454 cg17491947 cg17494781cg17498803 cg17500103 cg17500189 cg17507573 cg17509349 cg17512353cg17515747 cg17517900 cg17521338 cg17522907 cg17526300 cg17526812cg17527574 cg17528648 cg17530977 cg17532976 cg17533458 cg17534770cg17536603 cg17546469 cg17549121 cg17557106 cg17558973 cg17560327cg17564074 cg17573068 cg17575811 cg17584804 cg17585197 cg17585343cg17587023 cg17589079 cg17592734 cg17599471 cg17607368 cg17615007cg17617527 cg17618595 cg17619993 cg17620121 cg17621259 cg17625506cg17640322 cg17641046 cg17651693 cg17651973 cg17652435 cg17655978cg17656165 cg17660078 cg17666096 cg17671608 cg17676607 cg17679608cg17679781 cg17683390 cg17684296 cg17687528 cg17689925 cg17690832cg17691292 cg17691309 cg17699947 cg17703990 cg17705658 cg17707984cg17708016 cg17710868 cg17712828 cg17715556 cg17716617 cg17718302cg17718457 cg17727989 cg17728851 cg17730484 cg17731261 cg17735983cg17737146 cg17739038 cg17745088 cg17748329 cg17752015 cg17755907cg17755923 cg17759086 cg17764507 cg17764989 cg17765304 cg17767542cg17767945 cg17769463 cg17771515 cg17773020 cg17773349 cg17773763cg17774347 cg17777708 cg17778441 cg17779790 cg17780956 cg17783982cg17784528 cg17793527 cg17799295 cg17800473 cg17805404 cg17807528cg17808823 cg17815252 cg17820890 cg17824240 cg17825384 cg17826375cg17829673 cg17831137 cg17838029 cg17838765 cg17839008 cg17839237cg17839259 cg17839314 cg17839359 cg17840061 cg17846785 cg17849956cg17853504 cg17853707 cg17860381 cg17861551 cg17864044 cg17870408cg17871739 cg17874478 cg17876581 cg17880199 cg17880859 cg17881660cg17883067 cg17885062 cg17885806 cg17886028 cg17887537 cg17887593cg17887993 cg17888837 cg17891194 cg17893474 cg17896129 cg17902573cg17903544 cg17904852 cg17905084 cg17909311 cg17910679 cg17910969cg17913259 cg17927777 cg17929997 cg17930878 cg17935875 cg17940251cg17942925 cg17945419 cg17947546 cg17953520 cg17957827 cg17958384cg17958423 cg17960051 cg17967970 cg17971531 cg17972708 cg17975002cg17976576 cg17978562 cg17984375 cg17985124 cg17990532 cg17990771cg17995867 cg17996329 cg17997310 cg18003214 cg18003762 cg18007850cg18022777 cg18023401 cg18028711 cg18029521 cg18032744 cg18035537cg18040241 cg18042229 cg18043078 cg18044385 cg18049676 cg18050543cg18051461 cg18058532 cg18058994 cg18064474 cg18064852 cg18066271cg18068240 cg18070061 cg18071006 cg18071147 cg18072629 cg18073970cg18077391 cg18078305 cg18078387 cg18085998 cg18088494 cg18088653cg18096253 cg18100007 cg18108008 cg18108362 cg18109777 cg18110535cg18111139 cg18113107 cg18113780 cg18113994 cg18114235 cg18117228cg18121355 cg18123319 cg18125479 cg18125549 cg18127204 cg18132916cg18137427 cg18137931 cg18138147 cg18139020 cg18139254 cg18140683cg18142615 cg18143243 cg18144285 cg18144296 cg18146873 cg18147366cg18149653 cg18149689 cg18152871 cg18153124 cg18156793 cg18159850cg18161100 cg18167179 cg18169128 cg18169385 cg18172823 cg18175036cg18175880 cg18176922 cg18180107 cg18181323 cg18182358 cg18190030cg18191418 cg18194354 cg18195080 cg18200760 cg18202861 cg18206459cg18209835 cg18217145 cg18219563 cg18221467 cg18223947 cg18228326cg18233405 cg18233416 cg18234709 cg18235026 cg18235573 cg18236665cg18239372 cg18240143 cg18244289 cg18244915 cg18245460 cg18245660cg18245890 cg18248022 cg18249625 cg18251612 cg18252360 cg18253802cg18254147 cg18274325 cg18277508 cg18279751 cg18285309 cg18287975cg18293833 cg18300848 cg18304472 cg18304969 cg18308176 cg18310412cg18312997 cg18315695 cg18319921 cg18320379 cg18323466 cg18324126cg18325992 cg18326610 cg18326657 cg18328206 cg18329349 cg18331004cg18333921 cg18335176 cg18335243 cg18344922 cg18345826 cg18349835cg18354742 cg18354948 cg18357526 cg18357908 cg18362509 cg18371506cg18373944 cg18375586 cg18379780 cg18382422 cg18384926 cg18387399cg18387516 cg18388891 cg18390181 cg18395675 cg18399321 cg18403606cg18411103 cg18411550 cg18411800 cg18413587 cg18414618 cg18415585cg18416022 cg18416881 cg18418651 cg18419164 cg18420512 cg18424064cg18424968 cg18425877 cg18427336 cg18428373 cg18429863 cg18430156cg18430208 cg18434848 cg18438124 cg18438546 cg18440316 cg18440902cg18442587 cg18443378 cg18444347 cg18449734 cg18449997 cg18451274cg18453621 cg18461950 cg18463001 cg18465515 cg18466674 cg18466859cg18468394 cg18470710 cg18473090 cg18473652 cg18476049 cg18481137cg18483529 cg18484034 cg18484166 cg18488430 cg18492647 cg18493027cg18495307 cg18497550 cg18499667 cg18499839 cg18503381 cg18504015cg18511445 cg18512156 cg18513970 cg18522413 cg18522518 cg18524952cg18527133 cg18528082 cg18530075 cg18530251 cg18532190 cg18532727cg18533833 cg18534992 cg18542177 cg18542829 cg18542992 cg18547611cg18555069 cg18556179 cg18560502 cg18562663 cg18562689 cg18563326cg18564099 cg18564808 cg18565473 cg18567992 cg18568653 cg18575209cg18575770 cg18576158 cg18577280 cg18583563 cg18584747 cg18585722cg18586470 cg18586919 cg18588052 cg18592273 cg18602314 cg18603154cg18607529 cg18608369 cg18608389 cg18610423 cg18610889 cg18616418cg18619591 cg18621091 cg18624348 cg18634060 cg18637244 cg18639180cg18642499 cg18643445 cg18647881 cg18649745 cg18649939 cg18652367cg18654377 cg18655441 cg18655782 cg18658397 cg18669346 cg18670721cg18671997 cg18676082 cg18676237 cg18680346 cg18682423 cg18689666cg18696692 cg18697982 cg18702108 cg18703913 cg18704768 cg18705240cg18711394 cg18713809 cg18714498 cg18721673 cg18729886 cg18744458cg18746451 cg18750086 cg18750167 cg18750433 cg18751588 cg18753811cg18754389 cg18759693 cg18760621 cg18760835 cg18762243 cg18762485cg18762849 cg18763720 cg18764015 cg18764513 cg18768453 cg18775012cg18782736 cg18784420 cg18786479 cg18787914 cg18789841 cg18791121cg18795395 cg18795469 cg18796531 cg18798248 cg18798264 cg18798750cg18798922 cg18798995 cg18799048 cg18800161 cg18800479 cg18801599cg18809855 cg18810347 cg18811597 cg18812668 cg18812980 cg18813353cg18815779 cg18818834 cg18821633 cg18828883 cg18832632 cg18833573cg18834712 cg18839746 cg18845832 cg18846140 cg18846362 cg18849621cg18849840 cg18851831 cg18852765 cg18853935 cg18855836 cg18859763cg18867659 cg18867902 cg18870712 cg18873530 cg18875716 cg18876786cg18877969 cg18879160 cg18881873 cg18882060 cg18884940 cg18894054cg18900649 cg18901116 cg18902978 cg18904891 cg18909973 cg18911592cg18923197 cg18925548 cg18925923 cg18932809 cg18934496 cg18935660cg18938674 cg18940113 cg18940588 cg18943599 cg18946117 cg18946478cg18948743 cg18950617 cg18956706 cg18958684 cg18960642 cg18961230cg18964732 cg18985581 cg18993701 cg18996590 cg19001794 cg19006060cg19008809 cg19009323 cg19013262 cg19015909 cg19016289 cg19019371cg19021328 cg19021466 cg19022827 cg19026207 cg19027571 cg19027852cg19029747 cg19033035 cg19036153 cg19037327 cg19037480 cg19038690cg19040026 cg19040163 cg19040667 cg19041648 cg19042950 cg19047942cg19053664 cg19055936 cg19058495 cg19067897 cg19068071 cg19070841cg19075346 cg19079546 cg19081437 cg19083459 cg19083779 cg19083871cg19085463 cg19089073 cg19089314 cg19089337 cg19096424 cg19099850cg19109608 cg19110523 cg19113326 cg19113641 cg19115805 cg19116006cg19116429 cg19117365 cg19118212 cg19118812 cg19121462 cg19132762cg19135230 cg19135706 cg19141563 cg19148866 cg19157162 cg19157243cg19162106 cg19165854 cg19170589 cg19174706 cg19176453 cg19177465cg19177783 cg19181244 cg19184963 cg19187185 cg19190217 cg19191454cg19191500 cg19201719 cg19202687 cg19208331 cg19214222 cg19216563cg19217692 cg19219577 cg19221959 cg19226872 cg19229344 cg19233518cg19235095 cg19238531 cg19239848 cg19245371 cg19246266 cg19247228cg19247350 cg19248242 cg19250101 cg19257356 cg19263700 cg19267325cg19267760 cg19268498 cg19274837 cg19275050 cg19282714 cg19285217cg19287220 cg19287277 cg19287823 cg19289599 cg19294653 cg19301658cg19302722 cg19302831 cg19304410 cg19306047 cg19306496 cg19313015cg19314945 cg19317413 cg19317517 cg19318095 cg19320275 cg19320564cg19320816 cg19321991 cg19323374 cg19326622 cg19335954 cg19342109cg19348622 cg19354750 cg19355186 cg19356117 cg19358349 cg19359398cg19360562 cg19363466 cg19365062 cg19367540 cg19370980 cg19375296cg19376090 cg19378602 cg19381368 cg19381780 cg19382545 cg19396601cg19397703 cg19399402 cg19410738 cg19416050 cg19423014 cg19427757cg19429323 cg19429466 cg19430897 cg19431241 cg19431448 cg19435033cg19435381 cg19439810 cg19442495 cg19444950 cg19445996 cg19446018cg19446838 cg19450036 cg19450714 cg19452853 cg19452953 cg19455142cg19458485 cg19461392 cg19470159 cg19484319 cg19486482 cg19488260cg19491151 cg19494762 cg19499754 cg19504308 cg19507527 cg19508191cg19508683 cg19509715 cg19509763 cg19510038 cg19510792 cg19511590cg19511664 cg19512521 cg19515317 cg19523118 cg19523213 cg19523287cg19526626 cg19529326 cg19530176 cg19530551 cg19532257 cg19533530cg19534879 cg19536404 cg19538890 cg19539421 cg19540689 cg19540702cg19544459 cg19546232 cg19551232 cg19554555 cg19557340 cg19557518cg19557537 cg19559519 cg19564877 cg19570171 cg19571046 cg19580847cg19584530 cg19586132 cg19587654 cg19589919 cg19590063 cg19590834cg19592942 cg19594156 cg19594691 cg19595107 cg19595234 cg19595835cg19596204 cg19597776 cg19600750 cg19603847 cg19605258 cg19610370cg19611175 cg19611817 cg19613400 cg19614698 cg19618483 cg19621580cg19623237 cg19625088 cg19628038 cg19630440 cg19631064 cg19634213cg19635869 cg19636656 cg19639530 cg19639622 cg19641582 cg19643211cg19645410 cg19646327 cg19649564 cg19649746 cg19650197 cg19651223cg19653161 cg19654209 cg19657814 cg19659987 cg19660063 cg19660239cg19666937 cg19670870 cg19670923 cg19671026 cg19672497 cg19674065cg19675664 cg19677203 cg19677522 cg19684323 cg19688887 cg19690766cg19694978 cg19698348 cg19704348 cg19710444 cg19712965 cg19714957cg19716887 cg19717235 cg19719133 cg19719311 cg19721115 cg19722720cg19732605 cg19734015 cg19734255 cg19734779 cg19737664 cg19737972cg19741704 cg19741945 cg19742538 cg19746861 cg19752094 cg19752627cg19753230 cg19753526 cg19753794 cg19754190 cg19758151 cg19760323cg19761115 cg19763319 cg19763809 cg19767215 cg19769164 cg19769850cg19769920 cg19774315 cg19776201 cg19777105 cg19777900 cg19778944cg19779670 cg19781248 cg19784910 cg19788429 cg19788600 cg19789653cg19790568 cg19791003 cg19791395 cg19792316 cg19793697 cg19799744cg19802241 cg19805311 cg19806750 cg19814400 cg19816938 cg19821582cg19830192 cg19835478 cg19839347 cg19840484 cg19841276 cg19844581cg19847643 cg19850348 cg19850565 cg19851242 cg19851715 cg19853229cg19853516 cg19853927 cg19856145 cg19859515 cg19863210 cg19863319cg19864007 cg19868007 cg19871631 cg19875368 cg19876775 cg19877419cg19878516 cg19884345 cg19891817 cg19891951 cg19894747 cg19895124cg19895492 cg19896612 cg19899882 cg19907326 cg19908534 cg19908577cg19909712 cg19910201 cg19910780 cg19913430 cg19913934 cg19918005cg19918758 cg19923798 cg19925054 cg19926395 cg19927510 cg19929189cg19932227 cg19934709 cg19936032 cg19936436 cg19936912 cg19939262cg19940065 cg19950090 cg19950455 cg19956166 cg19956712 cg19958866cg19962565 cg19963768 cg19967492 cg19967800 cg19969873 cg19974445cg19975936 cg19977494 cg19978209 cg19981263 cg19984781 cg19987611cg19990182 cg19993316 cg20006924 cg20009641 cg20010012 cg20012008cg20018912 cg20019019 cg20022589 cg20023354 cg20024192 cg20028058cg20028291 cg20034091 cg20038789 cg20039407 cg20041567 cg20043291cg20043969 cg20046964 cg20047732 cg20048050 cg20052718 cg20053454cg20055841 cg20062650 cg20064106 cg20065569 cg20074142 cg20077155cg20078119 cg20078466 cg20079899 cg20089538 cg20090551 cg20090558cg20092102 cg20094085 cg20094282 cg20094610 cg20097650 cg20098332cg20102397 cg20103107 cg20110591 cg20111643 cg20111875 cg20113619cg20115051 cg20120070 cg20122470 cg20123891 cg20126635 cg20126980cg20129313 cg20134241 cg20136584 cg20137045 cg20138711 cg20140398cg20145610 cg20145692 cg20147819 cg20156450 cg20163166 cg20164253cg20166532 cg20170831 cg20172280 cg20173072 cg20174277 cg20176285cg20187670 cg20188733 cg20194811 cg20196129 cg20199629 cg20200385cg20200553 cg20209956 cg20211422 cg20213228 cg20214853 cg20215622cg20216802 cg20220436 cg20224751 cg20227255 cg20227976 cg20234947cg20235051 cg20237707 cg20239740 cg20251161 cg20252022 cg20254725cg20259398 cg20259674 cg20263839 cg20267521 cg20267922 cg20275132cg20276585 cg20279673 cg20280350 cg20283582 cg20284440 cg20285514cg20289609 cg20291779 cg20294320 cg20296001 cg20296461 cg20298895cg20303232 cg20309565 cg20312821 cg20319091 cg20324884 cg20329084cg20331595 cg20334252 cg20337021 cg20338300 cg20341942 cg20342628cg20344434 cg20346165 cg20353070 cg20353465 cg20353496 cg20354430cg20360212 cg20361600 cg20363904 cg20364024 cg20366601 cg20367329cg20367852 cg20369763 cg20375220 cg20382233 cg20383078 cg20387387cg20390702 cg20391764 cg20392240 cg20392585 cg20402000 cg20402552cg20405584 cg20406635 cg20410810 cg20413471 cg20414262 cg20414996cg20419623 cg20421191 cg20430063 cg20432589 cg20439288 cg20445950cg20450979 cg20452212 cg20459111 cg20463033 cg20463862 cg20465143cg20466166 cg20467502 cg20468063 cg20473379 cg20473895 cg20474370cg20475917 cg20480233 cg20486897 cg20489541 cg20492121 cg20492951cg20499861 cg20500237 cg20500248 cg20502003 cg20506550 cg20509675cg20516472 cg20521527 cg20522401 cg20523393 cg20524425 cg20526672cg20528183 cg20529489 cg20530585 cg20531392 cg20531781 cg20534596cg20536263 cg20536512 cg20536716 cg20537507 cg20538314 cg20540942cg20541238 cg20546002 cg20550224 cg20553394 cg20554228 cg20559000cg20559608 cg20568196 cg20568227 cg20570410 cg20573396 cg20578175cg20587543 cg20590617 cg20591405 cg20593831 cg20594401 cg20597013cg20598560 cg20603260 cg20603609 cg20603637 cg20606062 cg20607331cg20607769 cg20612002 cg20617956 cg20620147 cg20620700 cg20621715cg20622089 cg20623371 cg20624451 cg20626099 cg20627835 cg20636912cg20638626 cg20642710 cg20643237 cg20646950 cg20649951 cg20650802cg20651988 cg20655405 cg20656261 cg20660627 cg20661176 cg20662616cg20665459 cg20666492 cg20675505 cg20678442 cg20679955 cg20680592cg20680754 cg20681578 cg20693580 cg20696478 cg20696912 cg20696985cg20699497 cg20702935 cg20710842 cg20716311 cg20717205 cg20718214cg20718350 cg20718643 cg20718724 cg20718727 cg20718816 cg20725157cg20735365 cg20736997 cg20738807 cg20743280 cg20749730 cg20749916cg20751795 cg20752795 cg20752818 cg20752903 cg20753294 cg20753954cg20755820 cg20757519 cg20762044 cg20766467 cg20767025 cg20770339cg20771178 cg20771287 cg20772101 cg20776240 cg20777247 cg20777920cg20779944 cg20788479 cg20792512 cg20795569 cg20797142 cg20800844cg20801056 cg20804700 cg20811266 cg20811804 cg20813081 cg20813965cg20814574 cg20817465 cg20817483 cg20819218 cg20822109 cg20823742cg20835725 cg20841596 cg20841906 cg20844545 cg20846212 cg20847625cg20852605 cg20852890 cg20856064 cg20857455 cg20860638 cg20861607cg20864389 cg20868518 cg20869501 cg20873718 cg20875887 cg20877313cg20879085 cg20880234 cg20884939 cg20884984 cg20885078 cg20885578cg20890313 cg20891565 cg20892135 cg20896631 cg20901246 cg20910102cg20912211 cg20914464 cg20918484 cg20919556 cg20919922 cg20923716cg20927059 cg20927575 cg20930060 cg20936013 cg20937016 cg20938157cg20940153 cg20941820 cg20948024 cg20950465 cg20950724 cg20954537cg20954960 cg20954975 cg20959523 cg20961469 cg20966551 cg20971220cg20973347 cg20973396 cg20974077 cg20981058 cg20981182 cg20984053cg20984663 cg20991347 cg21007190 cg21007414 cg21010450 cg21011425cg21012362 cg21022732 cg21026553 cg21026663 cg21029201 cg21029403cg21029447 cg21035222 cg21037314 cg21038156 cg21039652 cg21039778cg21041580 cg21046967 cg21048422 cg21051989 cg21052325 cg21053015cg21056992 cg21057228 cg21057435 cg21057613 cg21057907 cg21062931cg21064080 cg21064182 cg21067341 cg21067540 cg21068911 cg21073859cg21074855 cg21076935 cg21079345 cg21080140 cg21085190 cg21088534cg21088686 cg21088896 cg21092303 cg21092867 cg21099488 cg21102477cg21106899 cg21107549 cg21108220 cg21116267 cg21117673 cg21119525cg21121336 cg21122656 cg21127268 cg21128610 cg21138752 cg21139392cg21142738 cg21153697 cg21155380 cg21155461 cg21164440 cg21167167cg21167628 cg21172814 cg21174533 cg21176048 cg21176475 cg21177599cg21179457 cg21182454 cg21182461 cg21188989 cg21195037 cg21197973cg21204904 cg21205071 cg21210985 cg21212277 cg21217886 cg21218082cg21220536 cg21226534 cg21227040 cg21231789 cg21234195 cg21237313cg21239691 cg21240441 cg21240762 cg21241317 cg21241839 cg21243939cg21244846 cg21246783 cg21250296 cg21253692 cg21254450 cg21256706cg21257950 cg21258057 cg21259253 cg21262300 cg21264227 cg21270074cg21271681 cg21273703 cg21276197 cg21278102 cg21278806 cg21281951cg21284880 cg21285525 cg21285895 cg21287489 cg21291896 cg21292337cg21293611 cg21294424 cg21294471 cg21294935 cg21296037 cg21296749cg21302562 cg21303803 cg21305471 cg21309100 cg21310689 cg21311023cg21317132 cg21325732 cg21326301 cg21331473 cg21332729 cg21337826cg21338532 cg21340148 cg21344989 cg21346925 cg21348210 cg21348533cg21348752 cg21351102 cg21356535 cg21356837 cg21357621 cg21364562cg21370527 cg21379008 cg21380341 cg21385047 cg21388527 cg21389901cg21395152 cg21399685 cg21400015 cg21401647 cg21401951 cg21410080cg21415643 cg21421098 cg21421278 cg21424120 cg21424782 cg21425749cg21426387 cg21433912 cg21435336 cg21437481 cg21437521 cg21445541cg21446511 cg21448788 cg21451378 cg21452805 cg21454231 cg21455939cg21460402 cg21463740 cg21465360 cg21474955 cg21478177 cg21482403cg21483700 cg21486944 cg21487923 cg21490561 cg21495139 cg21504815cg21504961 cg21509551 cg21514086 cg21517320 cg21518432 cg21518938cg21520042 cg21521784 cg21526173 cg21529405 cg21530552 cg21533806cg21535106 cg21537230 cg21540437 cg21541030 cg21542248 cg21543150cg21546671 cg21548032 cg21548414 cg21548719 cg21549294 cg21553524cg21555796 cg21556309 cg21563078 cg21569090 cg21573601 cg21574404cg21574435 cg21576698 cg21578322 cg21581531 cg21581821 cg21583160cg21583226 cg21585733 cg21586412 cg21589191 cg21591173 cg21593941cg21594642 cg21595039 cg21595503 cg21596317 cg21600537 cg21602557cg21606777 cg21606780 cg21607453 cg21610368 cg21612046 cg21612207cg21614408 cg21616552 cg21617058 cg21620778 cg21621906 cg21623748cg21624739 cg21627760 cg21634064 cg21637763 cg21639387 cg21640587cg21642176 cg21644830 cg21645164 cg21649051 cg21653132 cg21653558cg21655844 cg21656801 cg21657876 cg21658235 cg21660960 cg21662160cg21662494 cg21662807 cg21667796 cg21671020 cg21673646 cg21678388cg21681396 cg21685266 cg21686379 cg21692936 cg21701531 cg21702128cg21702506 cg21709909 cg21712019 cg21715963 cg21725265 cg21725976cg21726284 cg21727178 cg21730448 cg21732915 cg21733154 cg21735376cg21741689 cg21742923 cg21746851 cg21747958 cg21760146 cg21761844cg21768566 cg21773967 cg21781600 cg21781828 cg21782376 cg21784768cg21785847 cg21786334 cg21788396 cg21789044 cg21789898 cg21793948cg21805221 cg21806022 cg21808250 cg21810188 cg21810621 cg21814278cg21816336 cg21816532 cg21817720 cg21818749 cg21825027 cg21826900cg21831898 cg21835622 cg21837443 cg21838880 cg21839138 cg21844316cg21850038 cg21850254 cg21851151 cg21852439 cg21857363 cg21859434cg21860629 cg21864259 cg21864996 cg21868134 cg21868211 cg21868774cg21870776 cg21873251 cg21873817 cg21875437 cg21877656 cg21881273cg21884905 cg21886612 cg21890726 cg21901718 cg21903324 cg21904937cg21908208 cg21908638 cg21908706 cg21913630 cg21915639 cg21916655cg21921619 cg21922468 cg21923442 cg21926402 cg21932368 cg21934564cg21940042 cg21949747 cg21951425 cg21953346 cg21962423 cg21962603cg21963643 cg21968324 cg21968580 cg21972318 cg21972430 cg21973449cg21981706 cg21985559 cg21986225 cg21986422 cg21989213 cg21992350cg21994267 cg21995919 cg22002075 cg22007163 cg22008625 cg22009488cg22010743 cg22014661 cg22021178 cg22021756 cg22023952 cg22027897cg22028542 cg22029189 cg22030419 cg22031336 cg22031392 cg22037077cg22037648 cg22038579 cg22043720 cg22049732 cg22052291 cg22054918cg22058708 cg22058855 cg22062265 cg22062659 cg22063966 cg22065733cg22066230 cg22072935 cg22084563 cg22085335 cg22086322 cg22087390cg22087450 cg22087659 cg22090773 cg22090863 cg22091297 cg22093503cg22094750 cg22096450 cg22101924 cg22105512 cg22106401 cg22108864cg22109064 cg22114393 cg22124479 cg22124493 cg22124678 cg22125569cg22127901 cg22129906 cg22131825 cg22135941 cg22140378 cg22143569cg22145401 cg22151941 cg22152328 cg22154024 cg22154616 cg22157239cg22162404 cg22163056 cg22168369 cg22170732 cg22174088 cg22174693cg22174841 cg22174844 cg22176353 cg22178761 cg22185268 cg22185451cg22188495 cg22189307 cg22190028 cg22190438 cg22196952 cg22199615cg22207272 cg22208012 cg22212238 cg22214565 cg22219248 cg22221222cg22225105 cg22225673 cg22231908 cg22234897 cg22239483 cg22241593cg22243662 cg22247277 cg22250498 cg22254299 cg22255664 cg22259606cg22260952 cg22264975 cg22277271 cg22277431 cg22287067 cg22287624cg22297146 cg22301073 cg22308501 cg22313495 cg22319682 cg22321089cg22321237 cg22322828 cg22322863 cg22326903 cg22329423 cg22330492cg22331017 cg22333836 cg22334000 cg22335490 cg22337605 cg22339356cg22340072 cg22346581 cg22349506 cg22349573 cg22352717 cg22356339cg22356934 cg22357700 cg22359664 cg22361914 cg22363670 cg22364262cg22366214 cg22366350 cg22367989 cg22370379 cg22371845 cg22376465cg22377142 cg22379207 cg22380033 cg22380533 cg22380652 cg22385719cg22385764 cg22388982 cg22389279 cg22389730 cg22392276 cg22395192cg22396033 cg22396057 cg22396792 cg22401066 cg22402852 cg22404450cg22409420 cg22410750 cg22411407 cg22411784 cg22415969 cg22416074cg22416253 cg22426944 cg22433285 cg22435300 cg22435982 cg22438525cg22439359 cg22441533 cg22447539 cg22454660 cg22456785 cg22457984cg22462193 cg22466425 cg22466550 cg22470850 cg22471230 cg22473312cg22478210 cg22478591 cg22481535 cg22485350 cg22487177 cg22488268cg22488998 cg22489277 cg22489583 cg22493809 cg22494001 cg22496665cg22496968 cg22497313 cg22498996 cg22499565 cg22500428 cg22509189cg22510582 cg22512464 cg22513924 cg22516651 cg22517656 cg22521696cg22528685 cg22529861 cg22535307 cg22535359 cg22539985 cg22541378cg22541911 cg22546686 cg22549268 cg22549986 cg22555744 cg22557029cg22559669 cg22560190 cg22560410 cg22561889 cg22564317 cg22569523cg22577136 cg22578125 cg22581219 cg22582113 cg22582862 cg22587602cg22588307 cg22593554 cg22609576 cg22610211 cg22614355 cg22615158cg22623967 cg22628500 cg22631459 cg22632069 cg22633988 cg22634689cg22636429 cg22637507 cg22637594 cg22638185 cg22639895 cg22647810cg22648949 cg22652406 cg22657536 cg22658660 cg22663545 cg22664307cg22665276 cg22669058 cg22669787 cg22678708 cg22683038 cg22684008cg22684968 cg22685123 cg22685245 cg22685975 cg22687239 cg22690294cg22693978 cg22694153 cg22694480 cg22696982 cg22698996 cg22699044cg22699279 cg22700091 cg22702331 cg22702707 cg22704326 cg22706420cg22709082 cg22709563 cg22710306 cg22710840 cg22711621 cg22711679cg22712329 cg22715021 cg22718431 cg22719559 cg22724500 cg22725460cg22727304 cg22728534 cg22731064 cg22736718 cg22737001 cg22737282cg22744680 cg22745781 cg22748740 cg22750155 cg22753515 cg22758700cg22761704 cg22762180 cg22762844 cg22778421 cg22781950 cg22782191cg22783358 cg22784589 cg22789900 cg22790257 cg22790973 cg22791632cg22794494 cg22795212 cg22795590 cg22797164 cg22797570 cg22798201cg22800581 cg22806837 cg22808175 cg22812733 cg22814737 cg22815953cg22816621 cg22819502 cg22819616 cg22822824 cg22826936 cg22828045cg22829821 cg22831269 cg22834096 cg22834653 cg22835852 cg22841258cg22845037 cg22845159 cg22847691 cg22849482 cg22853371 cg22853986cg22854679 cg22856512 cg22861279 cg22862480 cg22863209 cg22864416cg22865286 cg22865582 cg22865720 cg22866835 cg22870899 cg22874046cg22874858 cg22880757 cg22886237 cg22886512 cg22889755 cg22892328cg22896991 cg22902177 cg22902535 cg22905274 cg22906273 cg22906700cg22907739 cg22909769 cg22913249 cg22913933 cg22919784 cg22920665cg22922289 cg22923827 cg22924838 cg22927510 cg22927696 cg22929692cg22929808 cg22931738 cg22934970 cg22935262 cg22935432 cg22935821cg22936457 cg22936975 cg22937891 cg22938308 cg22939193 cg22939802cg22943762 cg22944461 cg22944662 cg22945872 cg22946876 cg22947959cg22948236 cg22949149 cg22951056 cg22952210 cg22955973 cg22956410cg22960185 cg22960186 cg22961275 cg22963915 cg22972972 cg22973319cg22974804 cg22976979 cg22977876 cg22987590 cg22991959 cg22994808cg22995684 cg22996534 cg22998421 cg23000811 cg23003258 cg23003534cg23007898 cg23008047 cg23008646 cg23009315 cg23015949 cg23017840cg23021477 cg23023820 cg23025942 cg23027521 cg23027583 cg23029526cg23032045 cg23033785 cg23033906 cg23037648 cg23042796 cg23045258cg23045719 cg23046903 cg23048068 cg23048455 cg23051578 cg23052615cg23057706 cg23057732 cg23061718 cg23062425 cg23067281 cg23068731cg23069167 cg23070026 cg23070111 cg23071808 cg23076299 cg23079217cg23080179 cg23081580 cg23083315 cg23084951 cg23089764 cg23091984cg23093462 cg23095615 cg23096553 cg23096644 cg23097143 cg23097155cg23097985 cg23100152 cg23103993 cg23107161 cg23108125 cg23109721cg23116322 cg23116658 cg23117778 cg23123909 cg23127249 cg23129170cg23130076 cg23131007 cg23134869 cg23138678 cg23139473 cg23140554cg23143233 cg23149560 cg23151421 cg23151862 cg23152885 cg23156742cg23164203 cg23164681 cg23168000 cg23171203 cg23181844 cg23183469cg23183906 cg23185921 cg23186104 cg23190994 cg23191118 cg23192644cg23197935 cg23198529 cg23198902 cg23204296 cg23206311 cg23207710cg23209255 cg23210365 cg23211714 cg23220551 cg23221885 cg23224120cg23225690 cg23226129 cg23227355 cg23228540 cg23232563 cg23232615cg23233141 cg23236404 cg23240013 cg23242697 cg23245297 cg23246978cg23248007 cg23250910 cg23252261 cg23256664 cg23257840 cg23261102cg23264625 cg23267450 cg23268677 cg23274680 cg23275673 cg23276878cg23278267 cg23281215 cg23282051 cg23282585 cg23291136 cg23291305cg23294388 cg23295127 cg23296010 cg23296408 cg23300289 cg23300368cg23306832 cg23310850 cg23317907 cg23318713 cg23318786 cg23319285cg23324824 cg23326228 cg23326811 cg23331220 cg23333513 cg23333878cg23335390 cg23337116 cg23338170 cg23338503 cg23341163 cg23344452cg23345097 cg23345500 cg23347273 cg23353893 cg23354716 cg23355674cg23356993 cg23357265 cg23358710 cg23359276 cg23359706 cg23360190cg23361355 cg23361368 cg23363014 cg23363911 cg23370536 cg23372128cg23372936 cg23391006 cg23393521 cg23397571 cg23400883 cg23402444cg23407507 cg23409774 cg23410069 cg23410627 cg23411725 cg23413066cg23416197 cg23418339 cg23419328 cg23421262 cg23423382 cg23424273cg23424766 cg23426054 cg23429507 cg23429696 cg23431892 cg23433527cg23433607 cg23434186 cg23435746 cg23437733 cg23448584 cg23455517cg23456144 cg23458341 cg23460835 cg23461800 cg23463144 cg23463742cg23464032 cg23465990 cg23468897 cg23469706 cg23473285 cg23474794cg23477406 cg23479561 cg23481605 cg23482397 cg23486345 cg23491124cg23493510 cg23497217 cg23497767 cg23498273 cg23498748 cg23498771cg23501467 cg23502565 cg23504463 cg23508052 cg23508667 cg23511613cg23513318 cg23513690 cg23514110 cg23514324 cg23515325 cg23516634cg23520075 cg23522427 cg23524341 cg23525853 cg23526973 cg23528625cg23530053 cg23532246 cg23532765 cg23533198 cg23537193 cg23543123cg23543615 cg23547515 cg23549225 cg23553119 cg23556533 cg23563443cg23565749 cg23570590 cg23572228 cg23574053 cg23581656 cg23584332cg23585337 cg23587805 cg23593537 cg23596620 cg23599104 cg23599559cg23601066 cg23604012 cg23607033 cg23609571 cg23609905 cg23612557cg23614229 cg23614979 cg23618638 cg23620052 cg23620228 cg23623270cg23634087 cg23634711 cg23637314 cg23642766 cg23647191 cg23651323cg23651502 cg23658326 cg23658744 cg23662501 cg23664459 cg23665973cg23668806 cg23669159 cg23670599 cg23674469 cg23677039 cg23680282cg23680448 cg23684074 cg23684807 cg23685151 cg23687194 cg23690804cg23695485 cg23695687 cg23698058 cg23700044 cg23702412 cg23705973cg23710492 cg23715700 cg23718026 cg23718924 cg23719207 cg23721712cg23722428 cg23728669 cg23730260 cg23730436 cg23733133 cg23733525cg23737229 cg23738624 cg23743449 cg23752691 cg23753247 cg23758016cg23760165 cg23761820 cg23763424 cg23764381 cg23773266 cg23773809cg23782001 cg23782616 cg23782734 cg23789846 cg23791611 cg23791764cg23792245 cg23799276 cg23801012 cg23801021 cg23801029 cg23807354cg23811464 cg23812393 cg23813514 cg23833588 cg23836621 cg23841903cg23847701 cg23849078 cg23849311 cg23851476 cg23851860 cg23855030cg23855505 cg23867081 cg23868141 cg23872082 cg23872756 cg23877401cg23881902 cg23882234 cg23883409 cg23889086 cg23890800 cg23891251cg23892336 cg23893997 cg23896874 cg23897733 cg23900774 cg23901063cg23904854 cg23905374 cg23905542 cg23910786 cg23912231 cg23912239cg23921342 cg23922718 cg23928824 cg23931421 cg23933381 cg23934533cg23936023 cg23937586 cg23939037 cg23942980 cg23943360 cg23947039cg23951198 cg23952189 cg23952663 cg23954057 cg23954629 cg23956966cg23957915 cg23963136 cg23966569 cg23967540 cg23967742 cg23973417cg23974921 cg23974944 cg23975375 cg23977631 cg23977954 cg23980468cg23982678 cg23986470 cg23989053 cg23990723 cg23991039 cg23991622cg23992449 cg23994051 cg23994917 cg23996123 cg23996829 cg24003542cg24004478 cg24009736 cg24014143 cg24016939 cg24018609 cg24024176cg24025896 cg24029414 cg24033330 cg24034347 cg24035210 cg24036126cg24037629 cg24037897 cg24039019 cg24041269 cg24044288 cg24045482cg24050511 cg24050613 cg24053587 cg24056307 cg24059115 cg24065044cg24067214 cg24068708 cg24070213 cg24072311 cg24072474 cg24072819cg24075738 cg24077593 cg24078363 cg24078451 cg24078985 cg24080313cg24082730 cg24083324 cg24083817 cg24084504 cg24085719 cg24087403cg24088775 cg24089133 cg24092470 cg24095353 cg24096925 cg24098927cg24098938 cg24100726 cg24101560 cg24102241 cg24102266 cg24102726cg24103837 cg24106943 cg24111955 cg24113782 cg24116259 cg24117376cg24119079 cg24120847 cg24122218 cg24128066 cg24128130 cg24130774cg24131748 cg24132791 cg24133836 cg24134219 cg24134479 cg24140030cg24148719 cg24150232 cg24158594 cg24161615 cg24163616 cg24164873cg24166694 cg24167118 cg24167603 cg24176678 cg24182470 cg24182831cg24184687 cg24186750 cg24188073 cg24188919 cg24190127 cg24190415cg24193383 cg24193855 cg24199987 cg24201964 cg24205633 cg24212855cg24216220 cg24217844 cg24218295 cg24219929 cg24227728 cg24229589cg24231037 cg24231716 cg24236085 cg24236887 cg24238564 cg24239014cg24241129 cg24241429 cg24242082 cg24244270 cg24251048 cg24253255cg24255728 cg24265747 cg24265806 cg24266238 cg24269434 cg24278138cg24280645 cg24283621 cg24284436 cg24287438 cg24290286 cg24293044cg24295125 cg24298255 cg24304249 cg24305381 cg24306353 cg24306585cg24310038 cg24311947 cg24313597 cg24319718 cg24321030 cg24323031cg24325204 cg24326232 cg24327262 cg24330818 cg24331598 cg24333469cg24334111 cg24334243 cg24334304 cg24334809 cg24335138 cg24336278cg24337151 cg24339193 cg24339897 cg24341498 cg24341944 cg24344214cg24344662 cg24352971 cg24361849 cg24365518 cg24369746 cg24370755cg24371216 cg24371225 cg24372550 cg24374861 cg24377330 cg24382801cg24383902 cg24385334 cg24385694 cg24385733 cg24386906 cg24396358cg24400921 cg24402603 cg24404479 cg24405179 cg24405617 cg24407243cg24407308 cg24407327 cg24409356 cg24415565 cg24419856 cg24422297cg24425021 cg24427504 cg24427993 cg24432048 cg24434959 cg24435401cg24437429 cg24437625 cg24438313 cg24441185 cg24444188 cg24447438cg24450494 cg24450582 cg24456130 cg24457126 cg24458474 cg24461964cg24463006 cg24468105 cg24468682 cg24474130 cg24481381 cg24482021cg24482053 cg24482234 cg24487076 cg24488059 cg24488602 cg24491766cg24494316 cg24495982 cg24496978 cg24497541 cg24505307 cg24507144cg24508611 cg24509668 cg24510700 cg24516147 cg24517380 cg24519084cg24521848 cg24524308 cg24525509 cg24525913 cg24526702 cg24528447cg24532523 cg24533678 cg24540521 cg24542441 cg24546984 cg24548682cg24548754 cg24553364 cg24561572 cg24564088 cg24568842 cg24569637cg24573719 cg24575245 cg24577420 cg24577594 cg24578679 cg24581326cg24583766 cg24588375 cg24592207 cg24593694 cg24594177 cg24596027cg24597158 cg24599205 cg24604604 cg24606935 cg24613083 cg24615528cg24616138 cg24623497 cg24625128 cg24626079 cg24629122 cg24630201cg24631970 cg24633756 cg24634422 cg24635156 cg24635468 cg24638647cg24638668 cg24641522 cg24641993 cg24648594 cg24650522 cg24651706cg24653100 cg24660030 cg24663663 cg24664861 cg24668364 cg24678700cg24679395 cg24685601 cg24692068 cg24695316 cg24699519 cg24700222cg24700663 cg24701809 cg24703168 cg24703717 cg24706932 cg24707051cg24709718 cg24710320 cg24710870 cg24713878 cg24714666 cg24718729cg24718846 cg24718866 cg24719487 cg24720571 cg24720583 cg24722346cg24727133 cg24731635 cg24732574 cg24732929 cg24733262 cg24733530cg24734586 cg24738779 cg24740026 cg24740509 cg24740531 cg24740568cg24742512 cg24742832 cg24743639 cg24745633 cg24746100 cg24748867cg24753200 cg24753473 cg24758426 cg24759994 cg24760848 cg24760922cg24765016 cg24768974 cg24769513 cg24769821 cg24770596 cg24770985cg24771349 cg24772828 cg24775172 cg24776469 cg24777065 cg24778383cg24783322 cg24785946 cg24788053 cg24789348 cg24789487 cg24791380cg24794347 cg24797187 cg24799047 cg24802771 cg24804172 cg24804544cg24806963 cg24807106 cg24808223 cg24809072 cg24810439 cg24810741cg24814784 cg24817430 cg24818145 cg24824266 cg24824417 cg24825262cg24826867 cg24832140 cg24837680 cg24838654 cg24843474 cg24855943cg24856140 cg24863791 cg24867142 cg24868134 cg24868201 cg24870497cg24871132 cg24876786 cg24877195 cg24878071 cg24880024 cg24884207cg24884572 cg24885126 cg24886770 cg24887139 cg24887416 cg24890043cg24890054 cg24895258 cg24895834 cg24896860 cg24899571 cg24906525cg24908499 cg24910410 cg24913324 cg24914278 cg24917627 cg24917799cg24919790 cg24922817 cg24924091 cg24924560 cg24928995 cg24929737cg24930223 cg24933173 cg24935773 cg24937696 cg24942272 cg24954684cg24957657 cg24965937 cg24973289 cg24975662 cg24976104 cg24980893cg24980994 cg24984301 cg24984523 cg24985459 cg24994173 cg24997589cg24997989 cg24999255 cg25008346 cg25010146 cg25010361 cg25012864cg25014117 cg25014411 cg25015504 cg25016127 cg25024676 cg25026703cg25031380 cg25033139 cg25037730 cg25038060 cg25038283 cg25042239cg25049387 cg25049597 cg25052970 cg25053531 cg25057269 cg25062812cg25066040 cg25066490 cg25067153 cg25071520 cg25072436 cg25073829cg25074071 cg25074185 cg25075147 cg25087487 cg25089142 cg25092328cg25092681 cg25094411 cg25097514 cg25098208 cg25103850 cg25104716cg25115993 cg25116125 cg25121332 cg25122590 cg25122820 cg25129152cg25130672 cg25130993 cg25137030 cg25137687 cg25141069 cg25141957cg25147139 cg25151274 cg25151353 cg25152368 cg25152631 cg25153601cg25155846 cg25157280 cg25158678 cg25160286 cg25160978 cg25161976cg25164495 cg25166381 cg25167838 cg25178399 cg25179291 cg25184481cg25192419 cg25195415 cg25202131 cg25202298 cg25202636 cg25203704cg25204440 cg25204543 cg25208479 cg25208969 cg25211525 cg25213928cg25214923 cg25215340 cg25217365 cg25220460 cg25220625 cg25221442cg25223355 cg25224568 cg25226768 cg25227274 cg25229114 cg25230111cg25232660 cg25236324 cg25241964 cg25244476 cg25246092 cg25248628cg25251738 cg25253217 cg25255293 cg25255795 cg25262044 cg25263140cg25267765 cg25268754 cg25270670 cg25272554 cg25275166 cg25276412cg25280092 cg25283609 cg25285794 cg25291355 cg25291387 cg25297849cg25300214 cg25300584 cg25301406 cg25303599 cg25307691 cg25308231cg25308427 cg25310909 cg25314902 cg25315855 cg25317664 cg25318301cg25320115 cg25322095 cg25325038 cg25325588 cg25326896 cg25327452cg25328795 cg25330361 cg25330514 cg25331703 cg25332377 cg25333258cg25337705 cg25338262 cg25338766 cg25339408 cg25344133 cg25346193cg25347098 cg25351036 cg25353930 cg25358770 cg25360929 cg25361907cg25365565 cg25365746 cg25367905 cg25369168 cg25371950 cg25372335cg25372568 cg25373063 cg25373100 cg25375340 cg25381667 cg25381711cg25386820 cg25389261 cg25389470 cg25395413 cg25402610 cg25402685cg25404025 cg25405984 cg25406166 cg25406735 cg25406872 cg25411849cg25421903 cg25422351 cg25423755 cg25426776 cg25428398 cg25433267cg25436849 cg25437410 cg25438415 cg25438963 cg25439973 cg25442239cg25445612 cg25449542 cg25449655 cg25450449 cg25450450 cg25451874cg25452974 cg25454116 cg25454890 cg25463470 cg25464457 cg25465938cg25468863 cg25471365 cg25471478 cg25475603 cg25480117 cg25485192cg25492350 cg25497529 cg25502618 cg25503881 cg25504443 cg25506797cg25506879 cg25508217 cg25510643 cg25511237 cg25511332 cg25513173cg25514947 cg25518386 cg25521086 cg25522355 cg25530246 cg25530601cg25532501 cg25532925 cg25533096 cg25533556 cg25537962 cg25538602cg25544146 cg25547957 cg25549230 cg25555336 cg25555424 cg25560333cg25561440 cg25563233 cg25565138 cg25565383 cg25567337 cg25568243cg25571757 cg25572367 cg25574812 cg25577023 cg25578609 cg25583128cg25583491 cg25587181 cg25587535 cg25590392 cg25594636 cg25594899cg25598530 cg25598672 cg25602490 cg25605601 cg25607321 cg25608973cg25611476 cg25616216 cg25618087 cg25619607 cg25623721 cg25623934cg25626312 cg25628481 cg25629572 cg25635144 cg25643819 cg25645687cg25649038 cg25655799 cg25660010 cg25667335 cg25670839 cg25672142cg25678532 cg25679366 cg25681618 cg25681688 cg25682653 cg25687071cg25689079 cg25693289 cg25701418 cg25705836 cg25714069 cg25720022cg25721451 cg25724441 cg25726414 cg25726789 cg25727572 cg25729826cg25734420 cg25734490 cg25734572 cg25741578 cg25741731 cg25741953cg25742540 cg25745651 cg25749267 cg25756166 cg25756435 cg25756853cg25758217 cg25760229 cg25765619 cg25767504 cg25767985 cg25771677cg25774276 cg25775832 cg25776555 cg25776856 cg25782003 cg25783173cg25786436 cg25789216 cg25793521 cg25793630 cg25794153 cg25794402cg25796631 cg25798122 cg25799020 cg25800082 cg25800170 cg25803642cg25804018 cg25804470 cg25804860 cg25805709 cg25808577 cg25808892cg25808906 cg25809635 cg25817261 cg25823419 cg25824543 cg25826913cg25830048 cg25831204 cg25832771 cg25834869 cg25835225 cg25835669cg25836094 cg25837710 cg25840208 cg25841943 cg25846285 cg25848557cg25851842 cg25859141 cg25868286 cg25868769 cg25868998 cg25870025cg25872752 cg25874034 cg25874421 cg25875316 cg25876509 cg25885108cg25887236 cg25889711 cg25901204 cg25910314 cg25912580 cg25912827cg25917510 cg25917621 cg25918303 cg25920483 cg25921512 cg25924096cg25924911 cg25925764 cg25927164 cg25928603 cg25931385 cg25934680cg25938977 cg25942031 cg25946952 cg25947544 cg25950369 cg25951582cg25954223 cg25955837 cg25958857 cg25966893 cg25968571 cg25970618cg25973895 cg25975626 cg25976563 cg25977958 cg25982140 cg25988717cg25989216 cg25989719 cg25990647 cg25996614 cg25999722 cg26002713cg26002784 cg26005082 cg26005761 cg26008007 cg26012941 cg26015176cg26015401 cg26016484 cg26020635 cg26025543 cg26031047 cg26031255cg26031541 cg26032101 cg26033293 cg26035323 cg26036993 cg26037945cg26039848 cg26040332 cg26045220 cg26049726 cg26049744 cg26056104cg26056348 cg26063563 cg26075208 cg26075747 cg26076412 cg26076750cg26079699 cg26080444 cg26087678 cg26090855 cg26097011 cg26099766cg26100711 cg26101410 cg26105156 cg26107850 cg26108678 cg26111157cg26111761 cg26113636 cg26114642 cg26118408 cg26119367 cg26124242cg26125811 cg26126052 cg26129310 cg26131754 cg26131803 cg26132084cg26135012 cg26135506 cg26137971 cg26144437 cg26146542 cg26147351cg26147657 cg26149167 cg26150071 cg26151087 cg26154342 cg26154670cg26161849 cg26162932 cg26180126 cg26186613 cg26187876 cg26198463cg26202915 cg26205131 cg26207201 cg26209058 cg26211360 cg26212496cg26219095 cg26220298 cg26220773 cg26224223 cg26224915 cg26225694cg26226802 cg26228351 cg26232715 cg26233374 cg26237168 cg26240185cg26243894 cg26245302 cg26245667 cg26246411 cg26248878 cg26256158cg26256916 cg26258845 cg26258944 cg26261793 cg26261973 cg26263773cg26268636 cg26268866 cg26269703 cg26270362 cg26273150 cg26274596cg26276294 cg26277730 cg26281453 cg26282655 cg26284638 cg26284982cg26286805 cg26288577 cg26293015 cg26297688 cg26298979 cg26302094cg26307820 cg26308113 cg26309111 cg26313152 cg26316082 cg26322231cg26324132 cg26325209 cg26325806 cg26327071 cg26327118 cg26331625cg26332534 cg26333513 cg26333822 cg26333837 cg26335281 cg26337020cg26337123 cg26338195 cg26339484 cg26344233 cg26349375 cg26349672cg26355004 cg26365299 cg26366048 cg26366107 cg26367591 cg26370608cg26375461 cg26376168 cg26384031 cg26386846 cg26386852 cg26387473cg26391350 cg26392989 cg26394825 cg26396617 cg26400840 cg26404669cg26408235 cg26410121 cg26410483 cg26412722 cg26422059 cg26425669cg26426142 cg26428136 cg26429856 cg26433722 cg26436315 cg26436330cg26444995 cg26445292 cg26448406 cg26448489 cg26450740 cg26457761cg26458288 cg26459419 cg26459700 cg26460816 cg26464411 cg26464889cg26465214 cg26466323 cg26466856 cg26473651 cg26477844 cg26478074cg26478485 cg26482665 cg26485174 cg26487082 cg26487948 cg26489994cg26494225 cg26495865 cg26498020 cg26499055 cg26517151 cg26519745cg26520120 cg26520722 cg26522240 cg26523670 cg26524541 cg26528620cg26529911 cg26532253 cg26533949 cg26534508 cg26535805 cg26538529cg26539524 cg26539593 cg26541920 cg26542888 cg26544277 cg26546557cg26548288 cg26549326 cg26551211 cg26551897 cg26560928 cg26561148cg26564040 cg26566415 cg26569144 cg26569469 cg26571942 cg26573321cg26575690 cg26575738 cg26577201 cg26577454 cg26580421 cg26580801cg26582768 cg26583041 cg26592281 cg26597539 cg26599209 cg26600608cg26600954 cg26605406 cg26605467 cg26606184 cg26606256 cg26612362cg26613140 cg26615127 cg26618965 cg26620450 cg26625629 cg26626525cg26635208 cg26635845 cg26640467 cg26642510 cg26644059 cg26647219cg26647312 cg26649005 cg26652447 cg26653400 cg26654790 cg26654994cg26660414 cg26660801 cg26664090 cg26664797 cg26667720 cg26672104cg26672688 cg26673436 cg26674800 cg26675876 cg26678852 cg26680047cg26680520 cg26681211 cg26685735 cg26691604 cg26705425 cg26705553cg26708548 cg26708817 cg26709356 cg26709950 cg26713507 cg26715571cg26717983 cg26717995 cg26720389 cg26725153 cg26725274 cg26729197cg26731119 cg26732691 cg26744375 cg26745332 cg26745551 cg26745770cg26754826 cg26756506 cg26757053 cg26768712 cg26770917 cg26774079cg26776069 cg26781150 cg26784596 cg26785250 cg26785617 cg26788916cg26789332 cg26789732 cg26789869 cg26790372 cg26791905 cg26792080cg26792116 cg26792755 cg26795730 cg26796283 cg26797372 cg26797585cg26798879 cg26799802 cg26800162 cg26800371 cg26802053 cg26802291cg26815291 cg26820118 cg26822161 cg26823584 cg26824467 cg26825751cg26825934 cg26826871 cg26831241 cg26832509 cg26834887 cg26838995cg26839117 cg26840598 cg26843430 cg26847805 cg26851796 cg26853987cg26857911 cg26859900 cg26861593 cg26862527 cg26864834 cg26870192cg26872138 cg26873311 cg26877715 cg26878816 cg26881527 cg26886307cg26886972 cg26887632 cg26891924 cg26892444 cg26896668 cg26899113cg26904700 cg26904914 cg26908611 cg26908755 cg26909954 cg26910092cg26912242 cg26912984 cg26915774 cg26917673 cg26918442 cg26919818cg26922444 cg26923908 cg26924294 cg26928603 cg26929012 cg26929348cg26932552 cg26933063 cg26942943 cg26943708 cg26948603 cg26953462cg26954625 cg26955196 cg26957677 cg26958597 cg26958806 cg26963029cg26966630 cg26966707 cg26967167 cg26967579 cg26968387 cg26970841cg26970847 cg26972058 cg26973488 cg26982364 cg26984626 cg26986911cg26987855 cg26988146 cg26988215 cg26988406 cg26988692 cg26988895cg26990660 cg26992213 cg26995744 cg26995992 cg26998900 cg26999505cg27002185 cg27003849 cg27004639 cg27009208 cg27011620 cg27014927cg27015174 cg27016990 cg27018185 cg27023953 cg27025752 cg27028168cg27029179 cg27030612 cg27031632 cg27032142 cg27036581 cg27037103cg27040423 cg27041875 cg27042000 cg27046492 cg27051954 cg27052403cg27056599 cg27060622 cg27061115 cg27062573 cg27062617 cg27063138cg27064266 cg27066284 cg27068170 cg27068490 cg27070869 cg27072012cg27072749 cg27073079 cg27078812 cg27080194 cg27080211 cg27082486cg27084026 cg27084903 cg27085904 cg27086020 cg27086773 cg27087057cg27088830 cg27090492 cg27093143 cg27099166 cg27099274 cg27099500cg27101125 cg27108362 cg27109877 cg27111970 cg27112897 cg27116061cg27116819 cg27116888 cg27117639 cg27121584 cg27123533 cg27123665cg27125972 cg27128984 cg27133864 cg27134223 cg27136241 cg27138195cg27139933 cg27140220 cg27143938 cg27147871 cg27151812 cg27154391cg27162464 cg27164044 cg27166527 cg27173322 cg27175294 cg27178940cg27180880 cg27182172 cg27192597 cg27194921 cg27197380 cg27199872cg27204739 cg27209110 cg27212234 cg27213352 cg27220968 cg27222172cg27223727 cg27225570 cg27229407 cg27230038 cg27233989 cg27240775cg27242945 cg27243389 cg27244585 cg27249178 cg27250841 cg27253670cg27254295 cg27257566 cg27258025 cg27258933 cg27266382 cg27269940cg27275523 cg27276115 cg27279904 cg27286572 cg27293549 cg27295197cg27295781 cg27299538 cg27303430 cg27304043 cg27304516 cg27306119cg27307206 cg27308557 cg27310163 cg27311227 cg27313572 cg27315243cg27319192 cg27323154 cg27326642 cg27326687 cg27326823 cg27327588cg27331401 cg27333693 cg27335720 cg27336178 cg27338377 cg27339550cg27340350 cg27346528 cg27347140 cg27351780 cg27352765 cg27355739cg27358097 cg27359731 cg27360003 cg27361727 cg27362103 cg27362222cg27363741 cg27364874 cg27371984 cg27372898 cg27375072 cg27380218cg27380819 cg27382164 cg27383277 cg27386292 cg27388983 cg27391396cg27392850 cg27395666 cg27395939 cg27405644 cg27405960 cg27410952cg27412987 cg27413430 cg27417717 cg27418586 cg27420520 cg27421939cg27422496 cg27425719 cg27425996 cg27430961 cg27432847 cg27433451cg27434954 cg27434993 cg27436264 cg27437806 cg27443310 cg27445400cg27447053 cg27447689 cg27447868 cg27448015 cg27449131 cg27449352cg27451672 cg27453606 cg27457941 cg27462418 cg27464311 cg27465275cg27465717 cg27466237 cg27469783 cg27475076 cg27476262 cg27476576cg27478700 cg27479418 cg27482605 cg27486637 cg27487839 cg27492749cg27496650 cg27502066 cg27504805 cg27506082 cg27506462 cg27507700cg27511208 cg27511255 cg27517702 cg27519691 cg27525037 cg27530629cg27533019 cg27534281 cg27536453 cg27538026 cg27545919 cg27546065cg27546736 cg27546949 cg27549834 cg27552287 cg27555382 cg27558479cg27558594 cg27559724 cg27560922 cg27564875 cg27565366 cg27565555cg27569040 cg27569822 cg27576755 cg27577527 cg27579532 cg27584828cg27586581 cg27586588 cg27591375 cg27594756 cg27597110 cg27599958cg27604626 cg27606464 cg27607338 cg27612364 cg27619163 cg27627570cg27629771 cg27632050 cg27633530 cg27635394 cg27636310 cg27637930cg27637948 cg27638597 cg27641141 cg27646469 cg27647384 cg27650778cg27654641 cg27655921 cg27658416 cg27659841 cg27665925

TABLE II CRC Panel cg20295442 cg20463526 cg26122980 cg09822538cg26212877 cg20560075 cg16601494 cg27555582 cg10864878 cg03384825cg22538054 cg11017065 cg13405887 cg09022943 cg19651223 cg16476975cg17228900 cg05527869 cg01051310 cg12348588 cg20329153 cg02970696cg02259324 cg15778437 cg07703462 cg25088758 cg04537567 cg17222500cg15490715 cg20219457 cg16300300 cg11979589 cg05051043 cg12940822cg01563031 cg22065614 cg13024709 cg15467646 cg18120376 cg19939997cg19824907 cg18683604 cg07188591 cg14175690 cg15658945 cg01938650cg20556517 cg13726682 cg06952671 cg06913330 cg22834653 cg05046525cg17035091 cg03419885 cg10512745 cg03976877 cg09667303 cg03401096cg01883425 cg17287235 cg02173749 cg11501438 cg04897742 cg14236735cg11855526 cg17768491 cg09498146 cg25730685 cg10236452 cg04184836cg04198308 cg10362542 cg14348439 cg17470837 cg11281641 cg17698295cg11666087 cg18587340 cg25798987 cg07976064 cg13101087 cg09975620cg23217126 cg10457056 cg22623967 cg08430489 cg09740671 cg02043600cg24392818 cg25975712 cg03225817 cg26820055 cg18638914 cg00421139cg21672843 cg15384598 cg18884037 cg01419567 cg13554086 cg07974511cg07700514 cg23272632 cg16993043 cg01394819 cg23300368 cg16556906cg12816961 cg01947130 cg02604524 cg24487076 cg06528267 cg21938148cg03356747 cg16334314 cg20864608 cg03640756 cg13223402 cg04125371cg05209770 cg00843236 cg00662647 cg20079899 cg17029156 cg08558397cg08452658 cg01261798 cg04904331 cg03571927 cg08189989 cg15699267cg04790084 cg10058779 cg16918905 cg27200446 cg15015920 cg22879515cg16638385 cg02511156 cg02455397 cg27442308 cg20631014 cg00817367cg22474464 cg09802835 cg22871668 cg19875368 cg14098681 cg15779837cg08354093 cg14794428 cg15825786 cg12417685 cg04272632 cg21039708cg24033330 cg14485004 cg13690864 cg20012008 cg03133266 cg26274580cg20686234 cg03957481 cg04718428 cg14473327 cg15207742 cg12907379cg12042659 cg05374412 cg16676492 cg03755177 cg21314480 cg16230141cg10453425 cg26495865 cg05522774 cg10293925 cg10002178 cg02583633cg02539855 cg20443778 cg25012919 cg18786873 cg15461516 cg13867865cg09239744 cg09155997 cg09462445 cg14648916 cg13557668 cg09461837cg14936269 cg23697417 cg05171952 cg14409941 cg11428724 cg23932491cg05344430 cg19497031 cg16520288 cg09495977 cg14568217 cg21329599cg27111463 cg05758094 cg21875802 cg18355902 cg06997381 cg08434234cg19178853 cg07017374 cg02842227 cg15424739 cg21176643 cg23215729cg10096161 cg02483484 cg11859584 cg02174225 cg06651311 cg20450979cg06266613 cg15286044 cg17771605 cg09683824 cg16899920 cg07821427cg12859211 cg12686317 cg00625334 cg22284043 cg01878345 cg26990102cg24686074 cg16332256 cg04453180 cg24521633 cg16584573 cg05178576cg22878622 cg16729832 cg27264249 cg19752627 cg24773418 cg01419831cg18646207 cg16514543 cg18762727 cg03257575 cg13776340 cg16474297cg03698948 cg02058731 cg16482474 cg27364741 cg13562911 cg24305584cg15261247 cg26365854 cg11878331 cg04058593 cg18607529 cg06630204cg27101125 cg14725151 cg18759960 cg07057177 cg26615127 cg07068756cg11253514 cg24886267 cg27317433 cg24262066 cg18623980 cg11677857cg02869459 cg13619824 cg22138430 cg14657517 cg01579950 cg06172475cg16307705 cg23201032 cg14535068 cg07752026 cg24403845 cg01501819cg27493301 cg00114029 cg26739280 cg26818735 cg21901946 cg19320476cg26684946 cg23359394 cg27510832 cg00100121 cg26834169 cg00017221cg06319822 cg15409931 cg24876960 cg07078225 cg05562381 cg04156369cg07060006 cg16485558 cg07495363 cg17386213 cg07283152 cg11689407cg13432708 cg24599249 cg25767985 cg21678377 cg13464448 cg18406197cg11107669 cg16366473 cg07628404 cg14256587 cg14667871 cg23719318cg11732619 cg11821817 cg14965220 cg05228284 cg04171539 cg13368519cg07627556 cg20593611 cg17847723 cg11881754 cg06393563 cg19769760cg17483297 cg23978504 cg19924619 cg17263061 cg04100696 cg13652513cg12865552 cg26156687 cg07283114 cg02966153 cg09912350 cg20665002cg08157228 cg26232818 cg21583226 cg15344220 cg08460041 cg21325154cg14218042 cg03142956 cg13670601 cg05332960 cg26892444 cg25184481cg04689080 cg24134479 cg26547924 cg23462956 cg13882278 cg19003797cg11771234 cg04245057 cg13713293 cg02650317 cg01755562 cg10036918cg26029736 cg05710997 cg15867939 cg20168412 cg12424694 cg04549333cg22604123 cg11912330 cg23649435 cg06752260 cg16332936 cg18122419cg19631064 cg27541454 cg08708747 cg09849405 cg00854242 cg10417567cg01138867 cg07600871 cg01278387 cg01056653 cg23820770 cg05141147cg01505767 cg03324821 cg20611276 cg27308329 cg24870497 cg06978388cg19697475 cg23091984 cg12210736 cg16741041 cg26802291 cg09571420cg10173182 cg08386091 cg13353699 cg06499647 cg10188823 cg03336086cg16440629 cg22841810 cg27100436 cg25964032 cg09010323 cg09642925cg08095852 cg01135780 cg24034005 cg06500120 cg08105352 cg20949845cg11953272 cg13025668 cg16882226 cg26686277 cg04130185 cg06738356cg13796804 cg08311610 cg11161828 cg14018731 cg17958315 cg06554120cg07003632 cg08726248 cg27513573 cg17457560 cg26464221 cg13689003cg11733675 cg12850078 cg16589214 cg16434547 cg02979001 cg14602341cg21472506 cg09295081 cg01616178 cg12804010 cg27018185 cg00069860cg12993163 cg20401551 cg22898797 cg05127821 cg07244354 cg13643376cg08042975 cg16236766 cg00592781 cg17507573 cg10031614 cg20232102cg22723056 cg06480695 cg22147084 cg02629281 cg04242021 cg09628601cg17898329 cg14059768 cg16921310 cg13742526 cg08793877 cg05291069cg05624214 cg17838029 cg23048481 cg23226129 cg23582408 cg09424526cg15963563 cg05372242 cg16202470 cg24037897 cg17846334 cg24899571cg11911648 cg25987194 cg18147366 cg25649038 cg25905674 cg16478774cg11775528 cg04548336 cg07491495 cg09441363 cg03132773 cg21233688cg13448814 cg20642710 cg18013519 cg19519310 cg05619587 cg04215672cg11021744 cg26105156 cg26298979 cg02370417 cg23657299 cg24461964cg14088357 cg22403273 cg05812269 cg07559273 cg21098557 cg10903451cg07701191 cg26917673 cg01108106 cg23288962 cg09807215 cg12745764cg07204550 cg20276585 cg08592707 cg23108709 cg12661206 cg15927682cg07455757 cg08943428 cg13911723 cg13482432 cg06371502 cg22685369cg08867893 cg26306372 cg00745606 cg14867604 cg20803857 cg15245095cg11642382 cg15363487 cg13207797 cg13768269 cg26858268 cg22260952cg20072442 cg01154046 cg13443605 cg08172445 cg25616216 cg16303846cg06164660 cg01003015 cg27047243 cg11497952 cg18645133 cg12510981cg27326452 cg27313572 cg07482935 cg26515460 cg07721203 cg08511440cg20650138 cg23991622 cg10254000 cg26077100 cg14042851 cg00024472cg15462174 cg02746869 cg06256858 cg13914094 cg20772101 cg13258563cg20536716 cg18514820 cg15991072 cg22685245 cg02825977 cg09342766cg21864259 cg20319091 cg12630714 cg06959514 cg15212349 cg27557378cg02655972 cg20018469 cg04205107 cg26729197 cg11257429 cg26985666cg18996590 cg14898116 cg08700032 cg12639324 cg04679031 cg16899351cg17293936 cg27147718 cg17498773 cg25130672 cg17999686 cg16482314cg18800085 cg01466678 cg06714320 cg03825010 cg07729537 cg22396057cg02975107 cg11306587 cg15602740 cg07085962 cg04282138 cg26370226cg15803122 cg13031432 cg22587479 cg06530338 cg12874092 cg25165358cg01349858 cg08791131 cg18175808 cg11244340 cg05338433 cg13420848cg26072759 cg02040433 cg15852572 cg23964057 cg14765646 cg14517743cg06270802 cg00984694 cg16086373 cg12097080 cg27230784 cg11163901cg10779644 cg04797985 cg00266322 cg16341592 cg27498114 cg11109374cg05627441 cg00262031 cg26444995 cg20686479 cg16805150 cg04028634cg03840467 cg06650115 cg09324514 cg12584684 cg17108819 cg18488855cg02829680 cg15343461 cg06841192 cg04610224 cg07852757 cg20209009cg02576219 cg11278204 cg07622696 cg08247376 cg04190807 cg01626326cg04819760 cg05245861 cg00758881 cg05006349 cg00095976 cg11189837cg10918202 cg15749748 cg10383447 cg13238990 cg05961935 cg24723331cg12975779 cg07224914 cg08092105 cg02588107 cg03696327 cg25622036cg08145617 cg08828403 cg00581595 cg02361557 cg07952047 cg07034660cg25602490 cg00557354 cg04194840 cg11698244 cg27376683 cg18255353cg06223466 cg04882995 cg12973591 cg01258201 cg08521987 cg22370480cg05203877 cg11137615 cg20230721 cg13631916 cg00584713 cg19025435cg00279790 cg14101302 cg24531255 cg24851364 cg14470398 cg04838988cg21252914 cg14361033 cg17338208 cg20915632 cg11724516 cg10205753cg26215967 cg04279973 cg14775114 cg02756106 cg26256401 cg06078334cg05874561 cg11226148 cg16934178 cg22289831 cg00238770 cg10756127cg12644264 cg05588496 cg22441533 cg16098981 cg15802898 cg21008602cg05525743 cg21187769 cg18302726 cg05238517 cg13769223 cg19265970cg13721429 cg03038003 cg13096260 cg02030008 cg12169536 cg02327123cg06412358 cg22149516 cg08979737 cg24280540 cg05932408 cg23282559cg02795515 cg03091010 cg16935295 cg18565783 cg16120828 cg13761440cg05235761 cg22932815 cg07146119 cg05293738 cg26341102 cg14449051cg04626565 cg08085954 cg20594401 cg02707869 cg07005294 cg00053373cg19918758 cg23089549 cg06531379 cg04336836 cg15308062 cg08372619cg01090834 cg09547173 cg14657834 cg15087147 cg22975913 cg01978558cg13565575 cg08739576 cg24424545 cg13485685 cg12478381 cg27034576cg19123296 cg05082965 cg07748540 cg18233786 cg20513548 cg25664438cg07071978 cg10556384 cg23749856 cg18237607 cg06639332 cg13346411cg09553380 cg22583333 cg25299895 cg01946574 cg05142765 cg05484788cg22871002 cg07135732 cg02500300 cg20250080 cg01562349 cg02855633cg19439399 cg10143067 cg13643796 cg02139871 cg10160975 cg19819145cg15647515 cg09479015 cg10416527 cg03004999 cg21219996 cg19523085cg17883458 cg17475987 cg03352776 cg14374754 cg17350006 cg18736279cg12741994 cg06976395 cg01259029 cg06459000 cg26525127 cg07557790cg03912954 cg01791410 cg07991951 cg06124528 cg02732202 cg24488602cg03802461 cg04207385 cg13436799 cg03774520 cg24141863 cg02753362cg18220921 cg18496247 cg17304222 cg20341985 cg13869401 cg04534926cg08980837 cg25468723 cg18714412 cg24813176 cg26756083 cg01190692cg22130145 cg22376688 cg22675486 cg06498720 cg00057434 cg10694781cg11292593 cg22795590 cg24354581 cg01018701 cg01466288 cg00651020cg27155954 cg18928153 cg13285637 cg04144226 cg13767755 cg25923450cg25214789 cg23589617 cg16793187 cg14242042 cg05310249 cg02489958cg02136132 cg05991314 cg20052751 cg16673106 cg26985446 cg14455998cg10737195 cg24732574 cg06392318 cg13652557 cg01463565 cg19096571cg24862668 cg06025835 cg03350814 cg01775414 cg09198448 cg25302419cg17968795 cg03766620 cg27573591 cg02908587 cg27138584 cg00854166cg26756506 cg01001098 cg06357925 cg01826682 cg05273205 cg25584626cg19540689 cg07749724 cg24588375 cg20985635 cg00752628 cg02875118cg25999867 cg24805239 cg23040064 cg19635869 cg01134282 cg15565872cg05423529 cg24593272 cg17434309 cg00235933 cg25749267 cg06166932cg01582473 cg19661610 cg22015128 cg25249613 cg07994622 cg07765161cg04856292 cg18379780 cg04010684 cg09717526 cg25990363 cg11399100cg05245226 cg07997493 cg22794494 cg13843613 cg13376598 cg22849427cg14670435 cg16465502 cg22349506 cg06384463 cg23475625 cg26832142cg24709718 cg11195082 cg03725852 cg01693063 cg20956278 cg26393713cg22679003 cg06374307 cg10571951 cg09276565 cg14598976 cg12158272cg25922637 cg25542041 cg06457317 cg12278754 cg04802694 cg04043591cg24368902 cg13045310 cg02084669 cg02648941 cg14956197 cg21604803cg12144689 cg15496063 cg25834415 cg17491091 cg01733438 cg23912429cg05725404 cg27496965 cg19575759 cg10290373 cg05729480 cg11973177cg04484415 cg01329309 cg26916297 cg15577178 cg21437548 cg26983469cg11640773 cg26777303 cg13870510 cg06085011 cg18853199 cg12155165cg27102864 cg23858558 cg27103296 cg11235663 cg25810857 cg13414916cg01084435 cg18719750 cg13875133 cg10737663 cg11468193 cg26063719cg05222604 cg25070637 cg11897314 cg08804846 cg10857221 cg14260889cg00785042 cg14538332 cg14868994 cg27372162 cg25715035 cg19170009cg05129348 cg04261408 cg03247892 cg24482234 cg20631104 cg00687686cg08229468 cg14625631 cg01375976 cg11117108 cg02621694 cg04942472cg09217878 cg10292139 cg02101773 cg23067351 cg01049530 cg05469759cg26514728 cg16673702 cg03167496 cg03954411 cg01941671 cg08384171cg18024479 cg03276479 cg17503456 cg13165472 cg05718036 cg04005075cg19784477 cg23356017 cg09639622 cg17870792 cg26699569 cg16812519cg22799321 cg27517823 cg21527132 cg17965926 cg01285706 cg26824423cg23141855 cg24862252 cg02134353 cg03840647 cg05069909 cg04858398cg23686014 cg10303967 cg07501233 cg14424049 cg07523148 cg05622686cg26739865 cg18030776 cg01578987 cg00723271 cg07321467 cg10146880cg14377593 cg14942501 cg05528102 cg12566138 cg19675063 cg05201312cg15649801 cg26842303 cg26988406 cg10886442 cg10790685 cg02036364cg10539069 cg15980656 cg08937573 cg09470640 cg23821329 cg01733271cg24084681 cg22137815 cg05931096 cg23214267 cg02236650 cg08367638cg07380959 cg14830748 cg02486351 cg14046986 cg19111999 cg02830555cg03333330 cg16962683 cg00187933 cg22082709 cg20198108 cg04808179cg09558850 cg14408978 cg19207487 cg09679945 cg06460869 cg26668272cg19854521 cg00446722 cg16404040 cg11989011 cg05151811 cg05333442cg13328713 cg12190613 cg08614481 cg03167951 cg08918274 cg01343363cg19103770 cg06254440 cg15090727 cg00146951 cg27113419 cg07603382

The selection of a hypermethylated site according to the present methodis defined as follows. For each clinical sample of a specific cancertype in a database, e.g., The Cancer Genome Atlas, the methylation levelis determined for each methylation site i from a starting set of sitesas described in the preceding paragraph. For instance, for each clinicalsample from a set of colon adenocarcinoma samples in The Cancer GenomeAtlas, the methylation state at each of the CpG sites listed in Tables Iand II is determined, and the mean methylation level at each site icalculated as described elsewhere in this application. In someembodiments, the methylation level can be determined as the fraction of‘C’ bases out of ‘C′+′U’ total bases at a target CpG site i followingthe bisulfite treatment. In other embodiments, the methylation level canbe determined as the fraction of ‘C’ bases out of ‘C′+′T’ total bases atsite i following the bisulfite treatment and subsequent nucleic acidamplification. The mean methylation level at each site is then evaluatedto determine if one or more threshold is met. In some embodiments, athreshold selects those sites having the highest-ranked mean methylationvalues for a specific cancer type. For example, the threshold can bethose sites having a mean methylation level that is the top 50%, the top40%, the top 30%, the top 20%, the top 10%, the top 5%, the top 4%, thetop 3%, the top 2%, or the top 1% of mean methylation levels across allsites i tested for a specific cancer type, e.g., colon adenocarcinoma.Alternatively, the threshold can be those sites having a meanmethylation level that is at a percentile rank greater than orequivalent to 50, 60, 70, 80, 90, 95, 96, 97, 98, or 99. In otherembodiments, a threshold can be based on the absolute value of the meanmethylation level. For instance, the threshold can be those sites havinga mean methylation level that is greater than 99%, greater than 98%,greater than 97%, greater than 96%, greater than 95%, greater than 90%,greater than 80%, greater than 70%, greater than 60%, greater than 50%,greater than 40%, greater than 30%, greater than 20%, greater than 10%,greater than 9%, greater than 8%, greater than 7%, greater than 6%,greater than 5%, greater than 4%, greater than 3%, or greater than 2%.The relative and absolute thresholds can be applied to the meanmethylation level at each site i individually or in combination. As anillustration of a combined threshold application, one may select asubset of sites that are in the top 3% of all sites tested by meanmethylation level and also have an absolute mean methylation level ofgreater than 6%. The result of this selection process is a plurality oflists, one for each cancer type, of specific hypermethylated sites(e.g., CpG sites) that are considered the most informative for thatcancer type. These lists are then used to identify or classify a testgenomic DNA sample from a test organism, i.e. to determine whether thetest organism has a specific cancer type.

In the next step of the present method, a test genomic DNA sample from atest organism is analyzed by determining the methylation levels at eachsite i on the list of hypermethylated sites for each cancer type, andthese methylation levels for each site are then averaged to calculatethe average methylation level across the hypermethylated sites for eachcancer type. For instance, for each hypermethylated site i for colonadenocarcinoma, the methylation level at each site i on the list ofhypermethylated sites for colon adenocarcinoma is determined, and thesemethylation levels are then averaged to provide a single averagemethylation level. This process is repeated using the previously definedlists of hypermethylated sites for each of the cancer types, and resultsin a set of average methylation levels, each corresponding to adifferent cancer type. The average methylation levels are then rankedfrom lowest to highest. The cancer type corresponding to the highestaverage methylation level is considered to be associated with the testgenomic DNA, i.e. the cancer type is deemed to be present in the testorganism. It is understood that the normalized methylation difference orz-score also can be used in the present method instead of themethylation level at each CpG site.

For cancer screening or detection, the determination of a methylationlevel of a plasma (or other biologic) sample can be used in conjunctionwith other modalities for cancer screening or detection such as prostatespecific antigen measurement (e.g. for prostate cancer),carcinoembryonic antigen (e.g. for colorectal carcinoma, gastriccarcinoma, pancreatic carcinoma, lung carcinoma, breast carcinoma,medullary thyroid carcinoma), alpha fetoprotein (e.g. for liver canceror germ cell tumors) and CA19-9 (e.g. for pancreatic carcinoma).

Useful methylation sites that can be detected in a method set forthherein, for example, to evaluate cancer are include those present in theCancer Genome Atlas (see, for example, Cancer Genome Atlas ResearchNetwork et al., Nature Genetics 45:1113-1120 (2013)) or the selected CpGsites of the Pan Cancer Panel set forth in Table I (the listedmethylation sites are from Genome Build 37). Further examples of CpGsites that can be useful, for example, to identify or monitor colorectalcancer, are described in Worthley et al. Oncogene 29, 1653-1662 (2010)or set forth in Table II (the listed methylation sites are from GenomeBuild 37). Useful methylation markers for detection of ovarian cancerare set forth in US Pat. App. Pub. No. 2008/0166728 A1, which isincorporated herein by reference. All or a subset of the markers setforth herein and/or listed in a reference above can be used in a methodset forth herein. For example, at least 10, 25, 50, 100, 1×10³, 1×10⁴ ormore of the markers can be used.

Analysis of the methylation, prognosis or diagnosis information derivedfrom a method set forth herein can conveniently be performed usingvarious computer executed algorithms and programs. Therefore, certainembodiments employ processes involving data stored in or transferredthrough one or more computer systems or other processing systems.Embodiments of the invention also relate to apparatus for performingthese operations. This apparatus may be specially constructed for therequired purposes, or it may be a general-purpose computer (or a groupof computers) selectively activated or reconfigured by a computerprogram and/or data structure stored in the computer. In someembodiments, a group of processors performs some or all of the recitedanalytical operations collaboratively (e.g., via a network or cloudcomputing) and/or in parallel. A processor or group of processors forperforming the methods described herein may be of various typesincluding microcontrollers and microprocessors such as programmabledevices (e.g., CPLDs and FPGAs) and non-programmable devices such asgate array ASICs or general purpose microprocessors.

In addition, certain embodiments relate to tangible and/ornon-transitory computer readable media or computer program products thatinclude program instructions and/or data (including data structures) forperforming various computer-implemented operations. Examples ofcomputer-readable media include, but are not limited to, semiconductormemory devices, magnetic media such as disk drives, magnetic tape,optical media such as CDs, magneto-optical media, and hardware devicesthat are specially configured to store and perform program instructions,such as read-only memory devices (ROM) and random access memory (RAM).The computer readable media may be directly controlled by an end user orthe media may be indirectly controlled by the end user. Examples ofdirectly controlled media include the media located at a user facilityand/or media that are not shared with other entities. Examples ofindirectly controlled media include media that is indirectly accessibleto the user via an external network and/or via a service providingshared resources such as a “cloud.” A particularly useful cloud is onethat is configured and administered to store and analyze genetic datasuch as the BaseSpace™ service (Illumina, Inc. San Diego Calif.), orcloud services described in US Pat. App. Pub. Nos. 2013/0275486 A1 or2014/0214579 A1 (each of which is incorporated herein by reference).Examples of program instructions include both machine code, such asproduced by a compiler, and files containing higher level code that maybe executed by the computer using an interpreter.

In some embodiments, the data or information employed in the disclosedmethods and apparatus is provided in an electronic format. Such data orinformation may include reads derived from a nucleic acid sample,reference sequences, methylation states, patterns of methylation states,methylation difference scores, normalized methylation difference scores,aggregate coverage-weighted normalized methylation difference scores,methylation scores, coverage-weighted methylation scores, counselingrecommendations, diagnoses, and the like. As used herein, data or otherinformation provided in electronic format is available for storage on amachine and transmission between machines. Conventionally, data inelectronic format is provided digitally and may be stored as bits and/orbytes in various data structures, lists, databases, etc. The data may beembodied electronically, optically, etc.

In addition, certain embodiments relate to tangible and/ornon-transitory computer readable media or computer program products thatinclude instructions and/or data (including data structures) forperforming various computer-implemented operations. One or more of thesteps of a method set forth herein can be carried out by a computerprogram that is present in tangible and/or non-transitory computerreadable media, or carried out using computer hardware.

For example, a computer program product is provided and it comprises anon-transitory computer readable medium on which is provided programinstructions for steps of (a) obtaining a test data set that includes(i) methylation states for a plurality of sites from test genomic DNAfrom at least one test organism, and (ii) coverage at each of the sitesfor detection of the methylation states; (b) obtaining methylationstates for the plurality of sites in reference genomic DNA from one ormore reference individual organisms, (c) determining, for each of thesites, the methylation difference between the test genomic DNA and thereference genomic DNA, thereby providing a normalized methylationdifference for each site; (d) weighting the normalized methylationdifference for each site by the coverage at each of the sites, therebydetermining an aggregate coverage-weighted normalized methylationdifference score, and (e) storing or transmitting the aggregatecoverage-weighted normalized methylation difference score.

Methods disclosed herein can also be performed using a computerprocessing system which is adapted or configured to perform a method foridentifying methylation states or other characteristics of nucleicacids. Thus, in one embodiment, the invention provides a computerprocessing system which is adapted or configured to perform a method asdescribed herein. In one embodiment, the apparatus comprises a nucleicacid detection device, such as a nucleic acid sequencing device, adaptedor configured to determine methylation states and/or othercharacteristics of nucleic acids. The apparatus may also includecomponents for processing a sample from a test organism and/or referenceorganism. Such components are described elsewhere herein.

Nucleic acid sequence, methylation state, methylation pattern, or otherdata, can be input into a computer or stored on a computer readablemedium either directly or indirectly. In one embodiment, a computersystem is directly coupled to a nucleic acid detection device (e.g.sequencing device) that determines methylation states of nucleic acidsfrom samples. Data or other information from such tools are provided viainterface in the computer system. Alternatively, the methylation dataprocessed by systems are provided from a data storage source such as adatabase or other repository. Once available to the processingapparatus, a memory device or mass storage device buffers or stores, atleast temporarily, methylation states or other characteristics of thenucleic acids. In addition, the memory device may store methylationdifferences, normalized methylation differences, aggregate weightednormalized methylation differences, methylation scores, orcoverage-weighted methylation scores as described herein. The memory mayalso store various routines and/or programs for analyzing or presentingsuch information. Such programs/routines may include programs forperforming statistical analyses, etc.

In one example, a user provides a sample to a nucleic acid sequencingapparatus. Data is collected and/or analyzed by the sequencing apparatuswhich is connected to a computer. Software on the computer allows fordata collection and/or analysis. Data can be stored, displayed (e.g. viaa monitor or other similar device), and/or sent to another location. Thecomputer may be connected to the internet which is used to transmit datato a handheld device and/or cloud environment utilized by a remote user(e.g., a physician, scientist or analyst). It is understood that thedata can be stored and/or analyzed prior to transmittal. In someembodiments, raw data is collected and sent to a remote user orapparatus that will analyze and/or store the data. Transmittal can occurvia the internet, but can also occur via satellite or other connection.Alternately, data can be stored on a computer-readable medium and themedium can be shipped to an end user (e.g., via mail). The remote usercan be in the same or a different geographical location including, butnot limited to, a building, city, state, country or continent.

In some embodiments, the methods also include collecting data regardinga plurality of polynucleotide sequences (e.g., reads, tags and/ormethylation states) and sending the data to a computer or othercomputational system. For example, the computer can be connected tolaboratory equipment, e.g., a sample collection apparatus, a nucleotideamplification apparatus, a nucleotide sequencing apparatus, or ahybridization apparatus. The computer can then collect applicable datagathered by the laboratory device. The data can be stored on a computerat any step, e.g., while collected in real time, prior to the sending,during or in conjunction with the sending, or following the sending. Thedata can be stored on a computer-readable medium that can be extractedfrom the computer. The data that has been collected or stored can betransmitted from the computer to a remote location, e.g., via a localnetwork or a wide area network such as the internet. At the remotelocation various operations can be performed on the transmitted data asdescribed below.

Among the types of electronically formatted data that may be stored,transmitted, analyzed, and/or manipulated in systems, apparatus, andmethods disclosed herein are the following: reads obtained by sequencingnucleic acids in a test sample, methylation states for sites in thenucleic acids, one or more reference genome or sequence, methylationdifference score, normalized methylation difference score, aggregatecoverage-weighted normalized methylation difference score, methylationscore, or coverage-weighted methylation score as described herein.

These various types of data may be obtained, stored, transmitted,analyzed, and/or manipulated at one or more locations using distinctapparatus. The processing options span a wide spectrum. Toward one endof the spectrum, all or much of this information is stored and used atthe location where the test sample is processed, e.g., a doctor's officeor other clinical setting. Toward another extreme, the sample isobtained at one location, it is processed (e.g. prepared, detected orsequenced) at a second location, data is analyzed (e.g. sequencing readsare aligned) and methylation characteristics are determined at a thirdlocation (or several locations), and diagnoses, recommendations, and/orplans are prepared at a fourth location (or the location where thesample was obtained).

In various embodiments, the methylation data are generated on a nucleicacid detection apparatus (e.g. sequencing apparatus) and thentransmitted to a remote site where they are processed to determinemethylation characteristics. At this remote location, as an example,methylation difference score, normalized methylation difference score,aggregate coverage-weighted normalized methylation difference score,methylation score, or coverage-weighted methylation score can bedetermined. Also at the remote location, the methylation characteristicscan be evaluated to make a prognostic or diagnostic determination.

Any one or more of these operations may be automated as describedelsewhere herein. Typically, the detection of nucleic acids and theanalyzing of sequence data will be performed computationally. The otheroperations may be performed manually or automatically.

Examples of locations where sample collection may be performed includehealth practitioners' offices, clinics, patients' homes (where a samplecollection tool or kit is provided), and mobile health care vehicles.Examples of locations where sample processing prior to methylationdetection may be performed include health practitioners' offices,clinics, patients' homes (where a sample processing apparatus or kit isprovided), mobile health care vehicles, and facilities of nucleic acidanalysis providers. Examples of locations where nucleic acid detection(e.g. sequencing) may be performed include health practitioners'offices, clinics, health practitioners' offices, clinics, patients'homes (where a sample sequencing apparatus and/or kit is provided),mobile health care vehicles, and facilities of nucleic acid analysisproviders. The location where the nucleic acid detection takes place maybe provided with a dedicated network connection for transmittingsequence data (typically reads) in an electronic format. Such connectionmay be wired or wireless and may be configured to send the data to asite where the data can be processed and/or aggregated prior totransmission to a processing site. Data aggregators can be maintained byhealth organizations such as Health Maintenance Organizations (HMOs).

The analyzing operations may be performed at any of the foregoinglocations or alternatively at a further remote site dedicated tocomputation and/or the service of analyzing nucleic acid sequence data.Such locations include for example, clusters such as general purposeserver farms, the facilities of a genetic analysis service business, andthe like. In some embodiments, the computational apparatus employed toperform the analysis is leased or rented. The computational resourcesmay be part of an internet accessible collection of processors such asprocessing resources colloquially known as the “cloud”, examples ofwhich are provided elsewhere herein. In some cases, the computations areperformed by a parallel or massively parallel group of processors thatare affiliated or unaffiliated with one another. The processing may beaccomplished using distributed processing such as cluster computing,grid computing, and the like. In such embodiments, a cluster or grid ofcomputational resources collective form a super virtual computercomposed of multiple processors or computers acting together to performthe analysis and/or derivation described herein. These technologies aswell as more conventional supercomputers may be employed to processsequence data as described herein. Each is a form of parallel computingthat relies on processors or computers. In the case of grid computingthese processors (often whole computers) are connected by a network(private, public, or the Internet) by a conventional network protocolsuch as Ethernet. By contrast, a supercomputer has many processorsconnected by a local high-speed computer bus.

In certain embodiments, the diagnosis (e.g., determination that thepatient has a particular type of cancer) is generated at the samelocation as the analyzing operation. In other embodiments, it isperformed at a different location. In some examples, reporting thediagnosis is performed at the location where the sample was taken,although this need not be the case. Examples of locations where thediagnosis can be generated or reported and/or where developing a plan isperformed include health practitioners' offices, clinics, internet sitesaccessible by computers, and handheld devices such as cell phones,tablets, smart phones, etc. having a wired or wireless connection to anetwork. Examples of locations where counseling is performed includehealth practitioners' offices, clinics, Internet sites accessible bycomputers, handheld devices, etc.

In some embodiments, the sample collection, sample processing, andmethylation state detection operations are performed at a first locationand the analyzing and deriving operation is performed at a secondlocation. However, in some cases, the sample collection is collected atone location (e.g., a health practitioner's office or clinic) and thesample processing and methylation state detecting is performed at adifferent location that is optionally the same location where theanalyzing and deriving take place.

In various embodiments, a sequence of the above-listed operations may betriggered by a user or entity initiating sample collection, sampleprocessing and/or methylation state detection. After one or more ofthese operations have begun execution the other operations may naturallyfollow. For example, a nucleic acid sequencing operation may cause readsto be automatically collected and sent to a processing apparatus whichthen conducts, often automatically and possibly without further userintervention, the methylation state analysis and determination ofmethylation difference score, normalized methylation difference score,aggregate coverage-weighted normalized methylation difference score,methylation score, or coverage-weighted methylation score. In someimplementations, the result of this processing operation is thenautomatically delivered, possibly with reformatting as a diagnosis, to asystem component or entity that processes or reports the information toa health professional and/or patient. As explained, such information canalso be automatically processed to produce a treatment, testing, and/ormonitoring plan, possibly along with counseling information. Thus,initiating an early stage operation can trigger an end to end process inwhich the health professional, patient or other concerned party isprovided with a diagnosis, a plan, counseling and/or other informationuseful for acting on a physical condition. This is accomplished eventhough parts of the overall system are physically separated and possiblyremote from the location of, e.g., the sample collection and nucleicacid detection apparatus.

In some embodiments the results of a method set forth herein will becommunicated to an individual by a genetic counselor, physician (e.g.,primary physician, obstetrician, etc.), or other qualified medicalprofessional. In certain embodiments the counseling is providedface-to-face, however, it is recognized that in certain instances, thecounseling can be provided through remote access (e.g., via text, cellphone, cell phone app, tablet app, internet, and the like).

In some embodiments, disclosure of results to a medical professional orto a patient can be delivered by a computer system. For example, “smartadvice” systems can be provided that in response to test results,instructions from a medical care provider, and/or in response to queries(e.g., from a patient) provide genetic counseling information. Incertain embodiments the information will be specific to clinicalinformation provided by the physician, healthcare system, and/orpatient. In certain embodiments the information can be provided in aniterative manner. Thus, for example, the patient can provide “what if”inquiries and the system can return information such as diagnosticoptions, risk factors, timing, and implication of various outcomes.

In particular embodiments, the results or other information generated ina method set forth herein can be provided in a transitory manner (e.g.,presented on a computer screen). In certain embodiments, the informationcan be provided in a non-transitory manner. Thus, for example, theinformation can be printed out (e.g., as a list of options and/orrecommendations optionally with associated timing, etc.) and/or storedon computer readable media (e.g., magnetic media such as a local harddrive, a server, etc., optical media, flash memory, and the like).

It will be appreciated that typically such systems will be configured toprovide adequate security such that patient privacy is maintained, e.g.,according to prevailing standards in the medical field.

The foregoing discussion of genetic counseling is intended to beillustrative and not limiting. Genetic counseling is a well-establishedbranch of medical science and incorporation of a counseling componentwith respect to the methods described herein is within the scope andskill of the practitioner. Moreover, it is recognized that as the fieldprogresses, the nature of genetic counseling and associated informationand recommendations is likely to alter.

Example I Analytical Sensitivity of ctDNA Methylation-Based CancerDetection Using Aggregate Normalized Coverage-Weighted MethylationDifferences

This example describes a highly sensitive assay for detectingmethylation in circulating tumor DNA (ctDNA). Aberrant DNA methylationis a widespread phenomenon in cancer and may be among the earliestchanges to occur during oncogenesis. The assay described in this examplecan be useful for cancer screening.

The general approach applied here includes targeted methylationsequencing for multiple CpG sites affected in cancer.

Technical challenges addressed by the approach include providingultra-high sensitivity and specificity that benefits screeningapplications, providing a protocol for targeted methyl-seq from lowinput ctDNA, and providing bioinformatics algorithms for analysis ofmethylation levels across a large number of targeted sites.

Targeted Capture Probe Design

Two targeted methylation panels were developed. The Pan-Cancer Paneltargets 9,921 affected CpG sites in 20 major cancer types as selectedfrom The Cancer Genome Atlas Database. The CpG sites included in thePan-Cancer Panel are listed in Table I. The CRC Panel targets 1,162affected CpG sites in colorectal cancer. The CpG sites included in theCRC Panel are listed in Table II. The CpG sites listed in Table I andTable II refer to Genome Build 37.

The probe sequences for the CpG sites were selected from the InfiniumHM450 array (Illumina, Inc., San Diego, Calif.). Design principles forthe probes are shown in FIG. 1. Two probes were used for targets havinggreater than 4 CpG sites, including a completely methylated probe(having a G nucleotides that complements the C position of each CpGsite) and completely unmethylated probe (having an A nucleotide thatcomplements the U that is expected to result from bisulfite conversionof each of the C positions of a CpG site) as shown in FIG. 1. Incontrast, only one probe was used for targets having 4 or fewer CpGsites (the probe includes degenerate nucleotide R, complementary to U orC, at the C position of each CpG site).

Isolation and Extraction of cfDNA from Plasma

Plasma samples were obtained from human blood draws. Cell free DNA(cfDNA) was extracted using the QIAamp Circulating Nucleic Acid Kit(Qiagen, Hilden, Germany). Targeted ctDNA methylation sequencing wascarried out according to the workflow shown in FIG. 2, and as set forthbelow in the context of evaluating titration and detection sensitivity.

Titration and Detection Sensitivity

NA12878 genomic DNA was purchased from Coriell Institute (CoriellInstitute, Camden, N.J.), and LS1034 genomic DNA was purchased from ATCC(ATCC, Manassas, Va.). Genomic DNA was fragmented using Covaris M200(Covaris, Woburn, Mass.) and size-selected to 130-250 bp usingBluePippin (Sage Science, Beverly, Mass.) to simulate the sizedistribution of cfDNA. DNA quantification was performance usingQuant-iT™ PicoGreen® dsDNA Assay Kit (ThermoFisher Scientific, GrandIsland, N.Y.). 10%, 1%, or 0.1% LS1034 DNA was spiked into NA12878 DNAbackground to make the DNA mixtures. 30 ng of each mixture, 100%NA12878, or 100% LS1034 DNA was used in library preparation. Threereplicated libraries were generated for each titration level. A set ofsix replicates of NA12878 was used as the baseline reference genome.

Extracted cfDNA or sheared and size-selected genomic DNA was bisulfitetreated and purified using EZ DNA Methylation-Lightning Kit (ZymoResearch, Irvine, Calif.).

Bisulfite-seq Libraries were prepared using the Accel-NGS® Methyl-SeqDNA Library Kit (Swift Biosciences, Ann Arbor, Mich.).

Targeted capture was carried out on the bisulfite-seq libraries usingprobes that were complementary to fragments having the CpG sites listedin Table I or Table II. Capture probes were synthesized and biotinylatedat Illumina, Inc. Target capture was performed using Illumina TruSight™Rapid Capture Kit according to manufacturer's instructions except thatcustomized capture probes were used, and hybridization and wash stepswere performed at 48 C.

The products of the capture step were sequenced on an Illumina HiSeq2500 Sequencer using 2×100 cycle runs, with four samples in rapid runmode, according to manufacturer's instructions.

Bioinformatic Analysis

FASTQ sequences were demultiplexed followed by in silico demethylationwhereby all C's on read 1 were converted to T's and all G's on read 2were converted to A's. Subsequently, these “demethylated” FASTQsequences were aligned using BWA (v 0.7.10-r789) to an index comprisinga “demethylated” hg19 genome. BWA alignment is described in Li andDurbin (2010) Fast and accurate long-read alignment with Burrows-WheelerTransform. Bioinformatics, Epub. [PMID: 20080505], which is incorporatedherein by reference. Following alignment, the “demethylated” FASTQsequences were replaced with the original FASTQ sequences. Methylationlevels were calculated as the fraction of ‘C’ bases at a target CpG siteout of ‘C’+‘T’ total bases.

Following calculation of methylation levels at each CpG site for eachsample and replicate, aggregate coverage-weighted normalized methylationdifference z-scores were calculated as follows.

(1) the methylation level at each CpG site was normalized by subtractingthe mean methylation level in baseline and dividing by the standarddeviation of methylation levels in baseline to obtain a per-sitez-score. Specifically, the normalized methylation difference at each CpGsite was determined according to the formula:

$Z_{i} = \frac{\chi_{i} - \mu_{i}}{\sigma_{i}}$

where Z_(i) represents a normalized methylation difference for aparticular site identified as i, χ_(i) represents the methylation levelat site i in the test genomic DNA, μ_(i) represents the mean methylationlevel at site i in the reference genome, and σ_(i) represents thestandard deviation of methylation levels at site i in the referencegenomic DNA.

(2) the z-score at each CpG site was multiplied by the coverage observedat the CpG site, and the coverage-weighted z-score was then summedacross all CpG sites and then divided by the sum of the coverage squaredat each CpG site. More specifically, an aggregate coverage-weightedmethylation difference z-score (an example of an aggregatecoverage-weighted normalized methylation difference score, A) wasdetermined according to the formula:

$A = \frac{\sum_{i = 1}^{k}{w_{i}Z_{i}}}{\sqrt{\sum_{i = 1}^{k}w_{i}^{2}}}$

where w_(i) represents the coverage at site i and k represents the totalnumber of sites.

Results

A titration experiment was performed to demonstrate analyticalsensitivity using a colorectal cancer cell line LS1034 and a normal cellline NA12878. Namely, targeted ctDNA methylation sequencing wasperformed in triplicates using both the Pan-Cancer and CRC panels on0.1%, 1%, and 10% titrations of LS1034 into NA12878 along with pureLS1034 and pure NA12878. For each of the 15 sample replicates, theaggregate coverage-weighted methylation difference z-scores werecalculated using the normal NA12878 samples as the baseline (FIG. 3).The results indicate that the within sample variation is far less thanthe variation between titration levels. In particular, the clearseparation of the 0.1% titration of LS1034 into NA12878 from the NA12878sample indicates the assay and accompanying aggregate coverage-weightedmethylation difference z-score can achieve a 0.1% limit of detection.

Results obtained using the methods of this example provide highsensitivity evaluation of the cumulative effect of multiple affected CpGsites across the genome. By providing a method for detecting methylationpatterns the methods of this example can provide improved cancerdiagnosis than methods that rely on detection of somatic mutations, asevidenced by the improved concordance in alternations between CRC tissueand corresponding plasma when evaluating DNA methylation markerscompared to somatic mutations (see, for example, Danese et al.,“Comparison of Genetic and Epigenetic Alterations of Primary Tumors andMatched Plasma Samples in Patients with Colorectal Cancer” PLoS ONE10(5):e0126417. doi:10.1371/journal.pone.0126417 (2015), which isincorporated herein by reference). The methods described in this examplealso provide identification of tissue origin for cancer. Specifically,tissue specific methylation markers have been shown to be useful totrace the tissue origin of particular ctDNA sequences (see, for example,Sun et al. “Plasma DNA tissue mapping by genome-wide methylationsequencing for noninvasive prenatal, cancer, and transplantationassessments” Proc. Natl. Acad. Sci, USA 112 (40) E5503-E5512 (2015),which is incorporated herein by reference).

Example II Analytical Sensitivity of ctDNA Methylation-Based CancerDetection Using Coverage-Weighted Methylation Scores

This example describes an alternative highly sensitive assay fordetecting methylation in circulating tumor DNA (ctDNA). The assaydescribed in this example also can be useful for cancer screening,monitoring disease progression, or evaluating a patient's response to atherapeutic treatment.

Targeted Capture Probe Design

For this study, the two targeted methylation panels described in ExampleI were pooled together. The Pan-Cancer Panel targets 9,921 affected CpGsites in 20 major cancer types as selected from The Cancer Genome AtlasDatabase. The CpG sites included in the Pan-Cancer Panel are listed inTable I. The CRC Panel targets 1,162 affected CpG sites in colorectalcancer. The CpG sites included in the CRC Panel are listed in Table II.The combined CpG sites listed in Table I and Table II refer to GenomeBuild 37.

The probe sequences for the CpG sites were selected from the InfiniumHM450 array (Illumina, Inc., San Diego, Calif.). Design principles forthe probes are shown in FIG. 1. Two probes were used for targets havinggreater than 4 CpG sites, including a completely methylated probe(having a G nucleotides that complements the C position of each CpGsite) and completely unmethylated probe (having an A nucleotide thatcomplements the U that is expected to result from bisulfite conversionof each of the C positions of a CpG site) as shown in FIG. 1. Incontrast, only one probe was used for targets having 4 or fewer CpGsites (the probe includes degenerate nucleotide R, complementary to U orC, at the C position of each CpG site).

Isolation and Extraction of cfDNA from Plasma

Plasma samples were obtained from human blood draws. Cell free DNA(cfDNA) was extracted using the QIAamp Circulating Nucleic Acid Kit(Qiagen, Hilden, Germany). Targeted ctDNA methylation sequencing wascarried out according to the workflow shown in FIG. 2, and as set forthbelow in the context of evaluating titration and detection sensitivity.

Titration and Detection Sensitivity

As described above, NA12878 genomic DNA was purchased from CoriellInstitute (Coriell Institute, Camden, N.J.), and LS1034 genomic DNA waspurchased from ATCC (ATCC, Manassas, Va.). Genomic DNA was fragmentedusing Covaris M200 (Covaris, Woburn, Mass.) and size-selected to 130-250bp using BluePippin (Sage Science, Beverly, Mass.) to simulate the sizedistribution of cfDNA. DNA quantification was performance usingQuant-iT™ PicoGreen® dsDNA Assay Kit (ThermoFisher Scientific, GrandIsland, N.Y.). 10%, 1%, or 0.1% LS1034 DNA was spiked into NA12878 DNAbackground to make the DNA mixtures. 30 ng of each mixture, 100%NA12878, or 100% LS1034 DNA was used in library preparation. Threereplicated libraries were generated for each titration level. A set ofsix replicates of NA12878 was used as the baseline reference genome.

Extracted cfDNA or sheared and size-selected genomic DNA was bisulfitetreated and purified using EZ DNA Methylation-Lightning Kit (ZymoResearch, Irvine, Calif.).

Bisulfite-seq Libraries were prepared using the Accel-NGS® Methyl-SeqDNA Library Kit (Swift Biosciences, Ann Arbor, Mich.).

Targeted capture was carried out on the bisulfite-seq libraries usingprobes that were complementary to fragments having the CpG sites listedin Table I or Table II. Capture probes were synthesized and biotinylatedat Illumina, Inc. Target capture was performed using Illumina TruSight™Rapid Capture Kit according to manufacturer's instructions except thatcustomized capture probes were used, and hybridization and wash stepswere performed at 48 C.

The products of the capture step were sequenced on an Illumina HiSeq2500 Sequencer using 2×100 cycle runs, with four samples in rapid runmode, according to manufacturer's instructions.

Bioinformatic Analysis

FASTQ sequences were demultiplexed followed by in silico demethylationwhereby all C's on read 1 were converted to T's and all G's on read 2were converted to A's. Subsequently, these “demethylated” FASTQsequences were aligned using BWA (v 0.7.10-r789) to an index comprisinga “demethylated” hg19 genome. BWA alignment is described in Li andDurbin (2010) Fast and accurate long-read alignment with Burrows-WheelerTransform. Bioinformatics, Epub. [PMID: 20080505], which is incorporatedherein by reference. Following alignment, the “demethylated” FASTQsequences were replaced with the original FASTQ sequences. Methylationlevels were calculated as the fraction of ‘C’ bases at a target CpG siteout of ‘C’+‘T’ total bases.

After calculation of methylation levels at each CpG site for each sampleand replicate, coverage-weighted methylation scores were calculated asfollows.

(1) The methylation level at each CpG site was normalized by subtractingthe mean methylation level in baseline and dividing by the standarddeviation of methylation levels in the baseline to obtain a per-sitez-score. Specifically, the normalized methylation difference at each CpGsite was determined according to the formula:

$Z_{i} = \frac{\chi_{i} - \mu_{i}}{\sigma_{i}}$

where Z_(i) represents a normalized methylation difference for aparticular site identified as i, χ_(i) represents the methylation levelat site i in the test genomic DNA, μ_(i) represents the mean methylationlevel at site i in the reference genome, and σ_(i) represents thestandard deviation of methylation levels at site i in the referencegenomic DNA.

(2) The z-score for each CpG site i (Z_(i)) was converted into theprobability of observing such a z-score or greater by converting thez-score into a one-sided p-value (p_(i)). Probabilities were calculatedassuming a normal distribution, although other distributions (e.g.,t-distribution or binomial distribution) may be used as well.

(3) The p-value at each CpG site was weighted by multiplying the p-valueat each CpG site i (p_(i)) by the coverage observed at the CpG site(w_(i)), and a coverage-weighted methylation score (MS) was determinedby combining the weighted p-values according to the formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}{\ln\left( {w_{i}p_{i}} \right)}}}$

where p_(i) represents the one-sided p-value at site i, k represents thetotal number of sites, and w_(i) represents the coverage at site i.

Results

A titration experiment was performed to demonstrate analyticalsensitivity using a colorectal cancer cell line LS1034 and a normal cellline NA12878. Namely, targeted ctDNA methylation sequencing wasperformed in triplicates using the combined Pan-Cancer and CRC panels on0.1%, 1%, and 10% titrations of LS1034 into NA12878 along with pureLS1034 and pure NA12878. For each of the 15 sample replicates, thecoverage-weighted methylation scores were calculated using the normalNA12878 samples as the baseline (FIG. 4). The results indicate that thewithin sample variation is far less than the variation between titrationlevels. In particular, the clear separation of the 0.1% titration ofLS1034 into NA12878 from the NA12878 sample indicates the assay andaccompanying coverage-weighted methylation score can achieve a 0.1%limit of detection (see inset of FIG. 4).

Similar to the results in Example I, results obtained using the methodsof this example provide high sensitivity evaluation of the cumulativeeffect of multiple affected CpG sites across the genome. By providing analternative method for detecting methylation patterns, the methods ofthis example can provide a more sensitive cancer diagnosis than methodsrelying on detection of somatic mutations.

Example III Clinical Performance of ctDNA Methylation-Based CancerDetection Using Normalized Coverage-Weighted Methylation ScoreDifferences

This example evaluates clinical sensitivity and specificity of themethylation-based cancer detection in circulating tumor DNA (ctDNA)using normalized coverage weighted methylation score differences. Asnoted above, the assay described in this example can be useful forcancer screening, monitoring disease progression, or evaluating apatient's response to a therapeutic treatment.

Targeted Capture Probe Design

For this study, the two targeted methylation panels described in ExampleI were pooled together. The Pan-Cancer Panel targets 9,921 affected CpGsites in 20 major cancer types as selected from The Cancer Genome AtlasDatabase. The CpG sites included in the Pan-Cancer Panel are listed inTable I. The CRC Panel targets 1,162 affected CpG sites in colorectalcancer. The CpG sites included in the CRC Panel are listed in Table II.The combined CpG sites listed in Table I and Table II refer to GenomeBuild 37.

The probe sequences for the CpG sites were selected from the InfiniumHM450 array (Illumina, Inc., San Diego, Calif.). Design principles forthe probes are shown in FIG. 1. Two probes were used for targets havinggreater than 4 CpG sites, including a completely methylated probe(having a G nucleotides that complements the C position of each CpGsite) and completely unmethylated probe (having an A nucleotide thatcomplements the U that is expected to result from bisulfite conversionof each of the C positions of a CpG site) as shown in FIG. 1. Incontrast, only one probe was used for targets having 4 or fewer CpGsites (the probe includes degenerate nucleotide R, complementary to U orC, at the C position of each CpG site).

Blood Sample Collection and Processing

Cancer patients were recruited at MD Anderson Cancer Center (Houston,Tex.). A total of 70 blood samples collected from 63 late stage cancerpatients of three cancer types were used in this study (n=30 forcolorectal cancer (CRC), n=14 for breast cancer (BRCA), n=19 for lungcancer). Four CRC patients had blood samples collected at multiple timepoints. Three breast cancer samples and one colorectal cancer samplefailed sample quality control and therefore were excluded from theanalysis, resulting in the final set of 66 cancer samples (36 CRC, 11BRCA, and 19 lung), representing 59 different patients (29 CRC, 11 BRCA,and 19 lung). A total of 65 normal blood samples were collected fromhealthy subjects to be used as baseline methylation controls (20),training controls (20) and testing controls (25) as described herein.

Plasma was separated by centrifugation at 1600 G for 10 minutes. Thesupernatant was transferred to 15 mL centrifuge tubes and centrifuged atroom temperature for 10 minutes at 3000 G. The supernatant wastransferred to a fresh 15 mL centrifuge tube and stored in a freezer(−80° C.) and shipped on dry ice. Plasma samples from healthy donorswere obtained from BioreclamationIVT (Westbury, N.Y.). All samples werede-identified.

Isolation and Extraction of cfDNA from Plasma

Cell free DNA (cfDNA) was extracted using the QIAamp Circulating NucleicAcid Kit (Qiagen, Hilden, Germany). Targeted ctDNA methylationsequencing was carried out according to the workflow shown in FIG. 2,and as set forth below in the context of evaluating titration anddetection sensitivity.

Targeted bisulfite sequencing library preparation and sequencing

cfDNA was bisulfite treated and purified using EZ DNAMethylation-Lightning Kit (Zymo Research, Irvine, Calif.).

Whole genome amplification of bisulfite-converted DNA was performedusing Accel-NGS® Methyl-Seq DNA Library Kit (Swift Biosciences, AnnArbor, Mich.).

Targeted capture was carried out on the bisulfite-seq libraries usingprobes that were complementary to fragments having the CpG sites listedin Tables I and II. Capture probes were synthesized and biotinylated atIllumina, Inc. (San Diego, Calif.). Target capture was performed usingIllumina TruSight™ Rapid Capture Kit according to manufacturer'sinstructions. Hybridization and wash conditions were modified to yieldoptimal capture efficiency.

The products of the capture step were sequenced on an Illumina Hiseq2500Sequencer using 2×100 cycle runs, with four samples in rapid run mode,according to manufacturer's instructions.

Bioinformatic Analysis

FASTQ sequences were demultiplexed followed by in silico demethylationwhereby all C's on read 1 were converted to T's and all G's on read 2were converted to A's. Subsequently, these “demethylated” FASTQsequences were aligned using BWA (v 0.7.10-r789) to an index comprisinga “demethylated” hg19 genome. BWA alignment is described in Li andDurbin (2010) Fast and accurate long-read alignment with Burrows-WheelerTransform. Bioinformatics, Epub. [PMID: 20080505], which is incorporatedherein by reference. Following alignment, the “demethylated” FASTQsequences were replaced with the original FASTQ sequences. Methylationlevels were calculated as the fraction of ‘C’ bases at a target CpG siteout of ‘C’+‘T’ total bases.

After calculation of methylation levels at each CpG site for each sampleand replicate, coverage-weighted methylation scores were calculated asfollows.

(1) Methylation scores were initially determined for the training set of20 normal genomic DNA samples. First, a normalized methylationdifference (z-score) at a particular site i (e.g., CpG site) wasdetermined according to the formula:

$Z_{i} = \frac{\chi_{i} - \mu_{i}}{\sigma_{i}}$

wherein Z_(i) represents a normalized methylation difference for aparticular site identified as i, χ_(i) represents the methylation levelat site i in a member of the training set of normal genomic DNA, μ_(i)represents the mean methylation level at site i in the baseline samples,and σ_(i) represents the standard deviation of methylation levels atsite i in the baseline samples.

(2) The z-score for each CpG site i (Z₁) was converted into theprobability of observing such a z-score or greater by converting thez-score into a one-sided p-value (p_(i)). Probabilities were calculatedassuming a normal distribution, although other distributions (e.g.,t-distribution or binomial distribution) may be used as well.

(3) The p-value at each CpG site was weighted by multiplying the p-valueat each CpG site i (p_(i)) by the coverage observed at the CpG site(w_(i)), and a coverage-weighted methylation score (MS) was determinedby combining the weighted p-values according to the formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}{\ln\left( {w_{i}p_{i}} \right)}}}$

wherein p_(i) represents the one-sided p-value at site i, k representsthe total number of sites, and w_(i) represents the significance, forinstance coverage, of the site i.

(4) Statistical analysis of the training set methylation scores was thenperformed. The mean methylation score (μ_(MS)) and standard deviation ofmethylation scores (σ_(MS)) in the training set of normal genomic DNAwere calculated, characterizing the distribution of the methylationscore in a normal population.

(5) Next, methylation scores were determined for the 66 cancer genomicDNA samples and 25 testing controls. First, a normalized methylationdifference (z-score) at each CpG site was determined according to theformula:

$Z_{i} = \frac{\chi_{i} - \mu_{i}}{\sigma_{i}}$

where Z_(i) represents a normalized methylation difference for aparticular site identified as i, χ_(i) represents the methylation levelat site i in the test genomic DNA, μ_(i) represents the mean methylationlevel at site i in the reference genome, and σ_(i) represents thestandard deviation of methylation levels at site i in the referencegenomic DNA.

(6) The z-score for each CpG site i (Z_(i)) was converted into theprobability of observing such a z-score or greater by converting thez-score into a one-sided p-value (p_(i)). Probabilities were calculatedassuming a normal distribution, although other distributions (e.g.,t-distribution or binomial distribution) may be used as well.

(7) The p-value at each CpG site was weighted by multiplying the p-valueat each CpG site i (p_(i)) by the coverage observed at the CpG site(w_(i)), and a coverage-weighted methylation score (MS) was determinedby combining the weighted p-values according to the formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}{\ln\left( {w_{i}p_{i}} \right)}}}$

where p_(i) represents the one-sided p-value at site i, k represents thetotal number of sites, and w_(i) represents the coverage at site i.

(8) Finally, the methylation scores of the test genomic DNA samples wereevaluated against the distribution of methylation scores determined forthe training set population, represented by the mean methylation score(μ_(MS)) and standard deviation of methylation scores (σ_(MS)) for thetraining set of normal genomic DNA. The number of standard deviationsbetween the methylation score for the test genomic DNA and themethylation score mean (μ_(MS)) of the training set of normal genomicDNA was determined according to the formula:

$Z_{MS} = \frac{{MS} - \mu_{MS}}{\sigma_{MS}}$

wherein Z_(MS) represents a normalized methylation score difference, MSrepresents the methylation score of the test sample, μ_(MS) representsthe mean methylation score for the training set of normal genomic DNA,and σ_(MS) represents the standard deviation of methylation scores forthe training set of normal genomic DNA. A Z_(MS) value greater than 3standard deviations was used as a threshold to identify cancer samples.

Results

As noted above, the purpose of this experiment was to evaluate theclinical performance of the normalized coverage-weighted methylationscore difference algorithm, including its clinical sensitivity andspecificity. The 66 cancer samples and 25 normal samples were subjectedto the methylation score analysis as described herein, includingdetermining the z-score for each of the CpG sites listed in Tables I andII, converting the z-score into a one-sided p-value based on a normaldistribution assumption, weighting the p-values by coverage, andaggregating the individual weighted p-values into a single methylationscore using the Fisher formula. The resulting methylation scores wereused to distinguish the cancer samples from the normal samples. FIGS. 5and 6 show that the normalized coverage-weighted methylation scoredifference algorithm was able to detect 34 out of 36 CRC samples (94.4%sensitivity), 8 out of 11 BRCA samples (72.7% sensitivity), and 10 outof 19 lung cancer samples (52.6% sensitivity). The algorithm exhibited100% specificity, having correctly identified all 25 of the testingcontrol samples as normal.

Results obtained using the methods of this example provide highlysensitive and specific evaluation of the cumulative effect of multipleaffected CpG sites across the genome. By providing an alternative methodfor detecting methylation patterns, the methods of this example canprovide a more sensitive and specific cancer diagnosis than methodsrelying on detection of somatic mutations.

Example IV Clinical Performance of Cancer Type Classification MethodBased on Average Methylation Levels Across Preselected Subsets ofMethylation Sites

This example evaluates the clinical sensitivity of a method for cancertype classification based on average methylation levels acrosspreselected subsets of CpG methylation sites referred to herein as“hypermethylated” sites. The assay described in this example can beuseful for identifying the source of tumor in circulating cell-free DNA.

Correlation of Methylation Profiles Between Plasma and Tissue DNASamples

As an initial inquiry, we set out to determine how well the methylationprofiles of circulating tumor DNA (ctDNA) isolated from plasma samplescorrelated to those of DNA isolated from tumor tissues. A high degree ofcorrelation would lend credence to the idea that methylation profiles ofcfDNA can be used to classify the tumor of origin. To this end, wecompared the methylation profiles of the colorectal, breast and lungcancer samples that were detected in Example III to the averagemethylation profiles for each of the 32 cancer types from TCGA (TheCancer Genomic Atlas) that had a minimum of 30 cancer samples in thedatabase. The methylation profiles were determined substantially asdescribed in Examples I-III and consisted of methylation levels at 9,242CpG sites (poorly performing methylation sites from the original CpGpanels were filtered out to improve accuracy).

The comparison was performed in a pairwise manner between eachcancer-positive plasma sample from Example III and each of the 32 cancertype from TCGA, resulting in correlation coefficients ranging from 0to 1. The correlations were plotted as a two-dimensional correlationmap, which is shown in FIG. 7. The darker areas of the map correspond tohigher correlations, whereas the lighter areas of the map signify lowercorrelations. The observed correlations between the methylation profileswere generally highest for the matching tumor types. For example, in thebreast cancer samples from plasma, the correlation was highest to thebreast cancer tissue (breast invasive carcinoma), and lower in all othertumor tissue types. Similarly, for the CRC plasma samples, thecorrelation was highest to colon and rectum tissues (e.g., colonadenocarcinoma, esophageal carcinoma, rectum adenocarcinoma and stomachadenocarcinoma). The correlation was less pronounced in the lung cancersamples.

Development and Testing of Cancer Type Classification

Having determined that there is a significant correlation betweenmethylation profiles of ctDNA and DNA from tumor tissues, we proceededto develop and test a cancer type classification method in silico.

First, we identified 24 cancer types with more than 100 samples in theTCGA database. For each of these types, we created a list of“hypermethylated” sites, which were defined as sites having a meanmethylation level (across samples) in the top 3% across the entire paneland greater than 6% in terms of absolute values.

Given a test sample, we determined its cancer types in a three-stepprocess. First, for each of the 24 cancer types, the methylation levelsfor each of the “hypermethylated” sites on the list were determined asdescribed in Examples I-III. Next, the average methylation level acrossthe “hypermethylated” sites were calculated for each of the 24 cancertypes. Finally, each of the 24 cancer types was ranked by their averagemethylation levels across the “hypermethylated” sites and classified thetest sample by the cancer type with the highest average methylationlevel.

We then proceeded to back-test the method on each of the TCGA tissuesamples that was used to generate the lists of “hypermethylated” sites.Accuracy of the method was defined as the ratio of the number of cancersamples of a particular type that were identified correctly to the totalnumber of samples of that cancer type. Results of this analysis areshown in FIG. 8. As one can easily see from this figure, 22 out of 24cancer types were classified with over 75% accuracy. Indeed, many of thecancer types were correctly identified about 90% of the time or better.Only two types—esophageal carcinoma and testicular germ celltumors—failed to cross the 75% threshold.

Cancer Type Classification of Plasma Samples

The 52 plasma samples correctly identified as cancer samples in ExampleIII (34 CRC, 8 BRCA, and 10 lung) were subjected to the cancer typeclassification analysis as described above. Results of this analysis areshown in FIG. 9. The cancer classification algorithm correctlyidentified 28 out of 34 CRC samples (82%), 7 out of 8 BRCA samples (88%)and 7 out of 10 lung cancer samples (70%). These results demonstratethat the cancer type classification method described herein may be usedwith a high clinical sensitivity to identify the tissue of origin inctDNA from plasma samples.

Throughout this application various publications, patents or patentapplications have been referenced. The disclosures of these publicationsin their entireties are hereby incorporated by reference in thisapplication in order to more fully describe the state of the art towhich this invention pertains.

The term “comprising” is intended herein to be open-ended, including notonly the recited elements, but further encompassing any additionalelements.

Although the invention has been described with reference to the examplesprovided above, it should be understood that various modifications canbe made without departing from the invention. Accordingly, the inventionis limited only by the claims.

1-10. (canceled)
 11. A method for distinguishing an aberrant methylationlevel for DNA from a first cell type, the method comprising (a)providing, for a plurality of CpG sites in baseline genomic DNA from twoor more normal individual baseline organisms, a mean methylation leveland a standard deviation of methylation level for each CpG site in thebaseline genomic DNA; (b) providing a test data set comprising:methylation states for the plurality of CpG sites from a first testgenomic DNA from an individual test organism, wherein the CpG sites arederived from a sample, (c) determining, for each of the CpG sites, themethylation difference between the first test genomic DNA and thebaseline genomic DNA, thereby providing a normalized methylationdifference for each CpG site; and (d) converting the normalizedmethylation difference for each CpG site into a one-sided p-value; (e)determining an aggregate methylation score for the combination ofone-sided p-values for each CpG site for the first test genomic DNA. 12.The method of claim 11, wherein (a) comprises providing methylationstates for the plurality of CpG sites in the baseline genomic DNA fromthe two or more normal individual organisms, and determining, for eachof the CpG sites, the mean methylation level and standard deviation ofmethylation level for the baseline genomic DNA.
 13. The method of claim11, further comprising providing a second test data set comprising:methylation states for the plurality of CpG sites from a second testgenomic DNA from the individual test organism, and wherein the CpG sitesare derived from a sample; determining, for each of the CpG sites, themethylation difference between the second test genomic DNA and thebaseline genomic DNA, thereby providing a normalized methylationdifference for each CpG site for the second test genomic DNA; andconverting the normalized methylation difference for each CpG site forthe second test genomic DNA into a one-sided p-value; determining anaggregate methylation score for the combination of one-sided p-valuesfor each CpG site for the second test genomic DNA; and comparing theaggregate methylation score of the first test genomic DNA and the secondtest genomic DNA to determine whether or not a change has occurred inthe aggregate methylation score between the first and second testgenomic DNA.
 14. The method of claim 11, further comprising: (f)providing a training data set comprising: methylation states for theplurality of CpG sites from training genomic DNA from two or more normalindividual training organisms, wherein the CpG sites are derived from aplurality of different cell types from the normal individual trainingorganisms; (g) determining, for each of the CpG sites, the methylationdifference between each training genomic DNA from the normal individualtraining organisms and the baseline genomic DNA, thereby providing anormalized methylation difference for each CpG site for each traininggenomic DNA; (h) converting the normalized methylation difference foreach CpG site into a one-sided p-value; and (i) determining an aggregatemethylation score for the combination of one-sided p-values for each CpGsite for each training genomic DNA; (j) using the aggregate methylationscore for each training genomic DNA to calculate a mean aggregatemethylation score and a standard deviation of the aggregate methylationscores for the training genomic DNA, to result in a distribution of theaggregate methylation scores for the training genomic DNA; and (k)evaluating the aggregate methylation score of the first test genomic DNAagainst the distribution of the aggregate methylation scores for thetraining genomic DNA.
 15. The method of claim 11, wherein the normalizedmethylation difference at a particular CpG site is determined accordingto the formula: $Z_{i} = \frac{\chi_{i} - \mu_{i}}{\sigma_{i}}$ whereinZ_(i) represents a normalized methylation difference for a particularCpG site identified as i, χ_(i) represents the methylation level at CPGsite i in the first test genomic DNA or the training genomic DNA, μ_(i)represents the mean methylation level at CpG site i in the baselinegenome, and σ_(i) represents the standard deviation of methylationlevels at CpG site i in the baseline genomic DNA.
 16. The method ofclaim 14, wherein the evaluating of step (k) determines a normalizedmethylation score difference according to the formula:$Z_{MS} = \frac{{MS} - \mu_{MS}}{\sigma_{MS}}$ wherein Z_(MS) representsa normalized methylation score difference, MS represents the aggregatemethylation score of the first test genomic DNA, μ_(MS) represents themean methylation score for the training set of normal genomic DNA, andσ_(MS) represents the standard deviation of aggregate methylation scoresfor the training set of normal genomic DNA.
 17. The method of claim 11,wherein the aggregate methylation score (MS) is determined according tothe formula:${MS} = {{- 2}{\sum\limits_{i = 1}^{k}{\ln\left( p_{i} \right)}}}$wherein p_(i) represents the one-sided p-value at site i, and krepresents the total number of CpG sites.
 18. The method of claim 11,wherein the aggregate methylation score (MS) is determined according tothe formula:${MS} = {{- 2}{\sum\limits_{i = 1}^{k}{\ln\left( {w_{i}p_{i}} \right)}}}$wherein p_(i) represents the one-sided p-value at site i, k representsthe total number of CpG sites, and w_(i) represents coverage of the sitei.
 19. The method of claim 11, wherein the sample from the individualtest organism comprises circulating tumor DNA and circulating non-tumorDNA.
 20. The method of claim 11, wherein the sample comprises cell-freeDNA from blood.
 21. The method of claim 11, wherein the individual testorganism is a pregnant female and the first test genomic DNA comprisesgenomic DNA derived from somatic cells of the female and genomic DNAderived from somatic cells of prenatal offspring of the female.
 22. Themethod of claim 11, wherein the providing of the sample in step (b)comprises targeted selection of a subset of genomic DNA fragmentscomprising a set of predetermined target CpG sites.
 23. The method ofclaim 11, wherein the providing of the sample in step (b) furthercomprises treating the subset of genomic DNA fragments with bisulfite.24. The method of claim 11, wherein the providing of the sample in step(b) comprises detecting methylation states for the CpG sites.
 25. Themethod of claim 11, wherein the detecting methylation states for the CpGsites comprises a sequencing technique that sequentially identifiesnucleotides in the first test genomic DNA.
 26. The method of claim 16,wherein the first test genomic DNA is classified as having an aberrantmethylation level if the value of Z_(MS) is greater than
 3. 27-35.(canceled)